International Journal of Industrial and Systems Engineering (117 papers in press)

Regular Issues

• The impact of difficulty and expensive financing on energy industry conservation and emission reduction  
  by Zihan Xia 
  Abstract: This article separates the issues of financing difficulties and high financing costs, and studies the impact of financing difficulties and high financing costs on the energy-saving and emission reduction behaviour of enterprises. We find that when the problem of difficult financing for enterprises exists, the approved loan amount positively affects the level of energy conservation and emission reduction through production volume; The level of energy conservation and emission reduction is not related to the loan interest rate. The fixed cost investment in energy conservation and emission reduction is a major factor for enterprises to take energy conservation and emission reduction measures. Even with financing difficulties, because of the existence of carbon taxes and subsidies, companies tend to adopt energy-saving and emission reduction measures. The heterogeneity of enterprise scale only exists when financing difficulties exist. Once financing difficulties are resolved, there is no heterogeneity in the impact of enterprise scale on energy conservation and emission reduction levels. This means that the energy conservation and emission reduction levels of enterprises of different scales are ultimately the same.
  Keywords: difficulties in financing; high cost of financing; energy conservation and emission reduction; carbon tax; subsidy.
  DOI: 10.1504/IJISE.2023.10061208
   
  
• Jordan's Future Renewable Energy Stability and Break-even Analysis Under Various Catalysts Using System Dynamics  
  by Samer Abaddi 
  Abstract: The social acceptability of photovoltaic (PV) systems contributes not only to the amount of power generated but also to the CO2 emissions reduction in Jordan. The effect of three catalysts; subsidy proportion, Word of Mouth (WOM) and advertising effectiveness is addressed in this piece of work, in addition to a forecast of the power generated and the CO2 emissions reduction by 2080. System dynamics (SD) is the fundamental approach of this study. Qualitative interviews and energy reports assisted the data collection process and simulation was conducted between 2020 and 2080. Six scenarios are hypothesised to facilitate the comparison between the catalyst's effects with the help of break-even point analysis. Jordan is expected to generate 1.845 Terra Wh (TWh) and 995.9 TWh of energy by 2040 and 2080, respectively. The CO2 emissions reduction is expected to cross 630 million tons by 2080. Advertising effectiveness was found to be the top catalyst that stimulates the power generated in Jordan followed by WOM. The quantitative models foster the policy makers towards investing in social acceptability dimensions toward achieving earlier equivalency of demand and supply. This is the first study in Jordan that develops break-even calculations at various levels of catalysts using SD.
  Keywords: system dynamics; SD; power generated; word of mouth; WOM; subsidy proportion; advertising effectiveness; Jordan.
  DOI: 10.1504/IJISE.2023.10063431
   
  
• Analyzing the Role of Multi-Agent Technology on High-Tech Manufacturing using AHP, DEMATEL, and TOPSIS  
  by Vikram Singh, Somesh Kumar Sharma 
  Abstract: High-tech product manufacturers operate in extremely sensitive environments and face challenges in meeting the quality standards of high-tech products. To address these challenges, this study aims analysing the impact of multi-agent technology (MAT) on the quality standards of high-tech manufacturing (HTM). The extensive literature was used to explore eight factors of HTM and forty-five variables of MAT. A hybrid multi-criteria decision-making technique was used to analyse the factors and variables. The HTM Process is a highly prioritised and impactful factor. Process monitoring, automatic customised test plans, adaptive agents, demand forecasting agents, and virtual manufacturing are the top five globally ranked variables. The findings of this article provide ranking order and determine the relationship between factors and variables for the integration of MAT in HTM. This bridging can assist designers in improving the design quality, manufacturers in increasing process quality standards of products, and market experts in selecting the potential market.
  Keywords: HTM; MAT; analytical hierarchy process; AHP; decision-making trail evaluation laboratory; DEMATEL; high-tech products; HTPs; technique for order preference by similarity to ideal solution; TOPSIS.
  DOI: 10.1504/IJISE.2023.10061604
   
  
• A Systematic Literature Network Analysis Approach to Assess the Topology of Modern-era Supply Chain Risk Management Research  
  by Leslie Dass, Sreerengan V.R. Nair, Georgy Kurien, Dr S. Kumar Chandar 
  Abstract: Over the past decade, there has been a significant increase in research on supply chain risk management (SCRM). This review uses a systematic literature network analysis to provide an overview of the SCRM research landscape, with emphasis on optimisation approaches, mathematical modelling tools, and the identification of seminal studies and relevant keywords used in SCRM research. However, there are few quantitative models that represent the relationship between supply chain surplus, sustainability, and resilience in SCRM literature. The study has limitations since it only sources from a single database, and more clarity is needed on the effectiveness of optimisation in SCRM, which can be further evaluated through case studies and empirical studies.
  Keywords: supply chain; risk management; optimisation; linear programming; resilience; sustainability; surplus; profitability.
  DOI: 10.1504/IJISE.2023.10061610
   
  
• Blockchain technology adoption in healthcare: a systematic review and conceptual framework  
  by Ashraf Abdou, Basma Ezzat, Sharif Mazen, Nagy Ramadan 
  Abstract: Recently, blockchain technology has attracted a lot of interest from different researchers and academics due to its unique properties like immutability, interoperability, and confidentiality. However, to date, their adoption in the healthcare sector is still very limited. Few studies applied a systematic literature review (SLR) for blockchain adoption in healthcare. In this research study, the first contribution is to identify the factors that influence the adoption of blockchain by applying the SLR approach, understand how these factors are interrelated, and discuss the main challenges of blockchain adoption. The findings demonstrated that, the unified theory of acceptance and use of technology (UTAUT), the technology acceptance model (TAM) and its extension were the most popular models used for blockchain adoption. Then, we identified the key research gaps and proposed a conceptual framework to address the identified gaps to be a reference and guide for organisations adopting blockchain in healthcare.
  Keywords: blockchain technology; healthcare; blockchain adoption; systematic literature review; SLR; UTAUT; technology acceptance model; TAM; TOE.
  DOI: 10.1504/IJISE.2023.10061699
   
  
• Portable Coconut Tree Climbing Device and its Analysis  
  by Ravi Kumar Mandava  
  Abstract: Coconut tree is one of the useful plant among all other plants. Due to the lack of coconut tree climbers worldwide, many coconut palm growers are not interested in cultivating coconut farming. Based on the above problem, numerous researchers have developed various climbing mechanisms. To overcome this problem a novel coconut tree climbing device (CTCD) was introduced which can climb the coconut tree up to the canopy. To check the deformation behaviour and generated stresses of various parts of the device in the present research work, the authors conducted dynamic analysis, such as modal, harmonic, and transient analysis in ANSYS 2021. Moreover, the dynamic properties of each component will also be tested under vibrational excitation. Therefore, one of the vibrational properties, that is, the natural frequency, is used to analyse the effect of transient loads and avoid the noise and vibration hazards in the components of the coconut tree climbing mechanism.
  Keywords: coconut tree climbing device; dynamic analysis; finite element method; ANSYS.
  DOI: 10.1504/IJISE.2023.10061804
   
  
• SDAPI: A Systematic Approach to Integrating Industry 4.0 and Lean Manufacturing for SME Improvement  
  by Hafsa El-Kaime, Saad Lissane Elha 
  Abstract: Many businesses, particularly small and medium-sized enterprises (SMEs), seek to improve productivity and reduce resource usage. Lean manufacturing (LM) is a popular method for optimising processes by eliminating non-value-added activities and improving efficiency and flexibility. However, in today's rapidly changing technological and market environment, companies must also adopt innovative production management approaches to stay competitive. The Fourth Industrial Revolution and related technologies offer the opportunity to take current manufacturing systems to the next level. While previous research has explored the concept of Lean 4.0, which combines Industry 4.0 and LM, there has been less focus on the relationship between methodological approaches and technological concepts. This research aims to fill this gap by presenting a methodological-technological framework for implementing Industry 4.0 technologies in SMEs in order to achieve the objectives of LM. The proposed methodology, called SDAPI, is developed through a literature reviews, it consists of five steps: specify, detect, analyse, propose, and implement.
  Keywords: framework; Industry 4.0; lean manufacturing; LM; Lean 4.0; small and medium-sized enterprises; SMEs.
  DOI: 10.1504/IJISE.2023.10061809
   
  
• Customer Behavior Analytics in A Supermarket in Taiwan Based on RFM Model  
  by Mei-Wei Huang, Hao-Wei Yang, Ming-Min Lo, Yung-Tai Tang, Hsin-Hung Wu 
  Abstract: Supermarkets need to use a data-driven approach to segment customers based on their purchase transactions to meet different customer needs in this highly competitive retail industry in Taiwan. This empirical study combines clustering techniques and RFM model to analyse member customers' transaction data from a database of a supermarket in Taiwan within a six-week period. The results showed that 5,410 member customers are grouped into loyal, new, and vulnerable customers. A one-way analysis of variance is performed to show these three groups of customers are statistically different. This research further explores the top 10 best-selling merchandise items in both purchase quantity and total money spent. Loyal customers need to focus on five merchandise items. New customers have eight out of ten best-selling merchandise items appeared in both purchase quantity and total money spent. Supermarket management need to pay more attention to these eight items for new customers in this supermarket.
  Keywords: customer behaviour; supermarket; RFM model; data-driven approach; loyal customer; new customer; vulnerable customer; best-selling merchandise items; Taiwan.
  DOI: 10.1504/IJISE.2023.10062080
   
  
• Ranking of factors affecting performance of manufacturing industry using Fuzzy MAUT technique  
  by Rajdeep Singh, Chandan Deep Singh 
  Abstract: With the rise of creative engineering, India's manufacturing industry is expanding quickly. Because of this, the market is more cutthroat for businesses, especially those that are indigenous. Core functional competences are essential for survival in the age of globalisation since they can positively or negatively impact a variety of organisational performance factors. This paper deals with the prioritisation or ranking of the factors which affect core functional competencies and further affect the performance of Indian manufacturing industry. For the ranking of the attributes fuzzy MAUT method has been used in the study.
  Keywords: fuzzy MAUT; core functional competencies; competitiveness; globalisation.
  DOI: 10.1504/IJISE.2023.10062495
   
  
• Particle Swarm of Optimisation Strategy for Design Optimisation of a Series-Parallel System Incorporating Failure Dependencies and Multiple Repair Teams  
  by Himani Pant, S.B. Singh 
  Abstract: A series-parallel system with multiple repair teams and failure dependence is investigated in this article. An optimal design problem is being scrutinised and worked upon in the current paper. This work is conducted in reference to prior study conducted by Hu et al. (2012). They used genetic algorithm (GA) to find the optimal design of the seriesparallel configuration consequently minimising its cost. The particle swarm optimisation (PSO) technique is being proposed in this article to further refine their results. The solution entails identifying the vector comprising of system components and repair teams, (n1, n2, , nN, r1, r2, , rN). These computations were carried out using the computer software Python. As a consequence, extremely intriguing results were achieved.
  Keywords: particle swarm optimisation; PSO; design optimisation; series-parallel configuration; failure dependencies.
  DOI: 10.1504/IJISE.2023.10062574
   
  
• Sustainable Spare Parts Inventory Stock Control Management at Macro Level, using Linear Programming: Perspective to Petroleum & Fertiliser Industries  
  by Sandeep Sharda, Sanjeev Mishra, Dheeraj Nimawat 
  Abstract: The goal of the study is to manage the problem in the petroleum and fertiliser sectors by optimising the overall spare parts inventory. To solve the issue, the proposed framework employs the linear programming model (LPM) and TORA software to optimise the entire spare parts inventory. This research offers petroleum and fertiliser industries a clear and straightforward way for the spare parts management. Results show improved cost and stock management that promotes sustainability with optimised data set of total spare parts inventory as 40,000 numbers and US$65.5 million. Additionally, it eliminates the excessive stock due to exaggerated risk with traditional practices and reduces deterioration by lowering long-stay of items in the warehouse. Validation of model is done using classified data sets (as HML and FSN) that are based on previous factual six years' cumulative consumption and acquired from an Indian fertiliser industry.
  Keywords: high; medium and low; HML; sustainability; spare parts macro inventory; linear programming; LP; TORA; fast; slow and non-moving; FSN.
  DOI: 10.1504/IJISE.2023.10062575
   
  
• A comprehensive approach to UA Facility Layout Design Using Genetic Algorithm  
  by Kamal Deep 
  Abstract: Facility layout panning is a quantum leap for the production industry to realise the low entropy, widely applied to the unequal area facility layout problems (UA-FLPs). This paper aims at the optimisation of UA-facility layout in the flexible bays structure (FBS) to maximise the adjacency requirements of facility types for the production layout. The FBS is a most commonly used structure flexible to allocate the facilities in the bays of unequal areas permitting empty space in the total area of the layout. The proposed mixed integer programming model has been formulated to ensure; minimum side length, confined aspect ratio of facility types, and optimal space utilisation in the total area of facility layout. The genetic algorithm based heuristic has been used to search the discrete solution space in a feasible time span. The optimal results obtained are mapped with the best known numerical instances reported in the literature to approve the efficacy of proposed solution approach.
  Keywords: unequal area facility layout; flexible-shape facilities; genetic algorithm-based optimisation algorithm; flexible bays structure; FBS.
  DOI: 10.1504/IJISE.2024.10062606
   
  
• Trajectory prediction using inference model  
  by Yun-xiang HAN 
  Abstract: With the rapid increase in air traffic, more accurate aircraft trajectory prediction is the focus of integrated airspace operations for both manned and unmanned civil aviation. The development of machine learning technology is expected to bring new solutions to this problem. This paper processes and analyses aircraft trajectory data, and models aircraft trajectory prediction based on hidden Markov models, providing an efficient and accurate solution for aircraft trajectory prediction. Firstly, the trajectory data was pre-processed to provide effective support for the subsequent model formulation. Secondly, a trajectory prediction model was designed using the trajectory data and hidden Markov model. Finally, the performance of different models was compared and analysed through experiments.
  Keywords: trajectory prediction; air traffic management; system modelling; simulation.
  DOI: 10.1504/IJISE.2023.10062927
   
  
• Multi-Objective Optimization in Turning AISI 304 Stainless Steel: An Integration of The Taguchi Method, Response Surface Methodology, and NSGA-II  
  by Cong Chi Tran, Thi Tham Nguyen, Van Tuu Nguyen 
  Abstract: This study examined the impact of machining parameters [depth of cut (d), feed rate (f), and spindle speed (s)] on surface roughness and material removal rate in the turning process of AISI 304 stainless steel. Three optimisation methods were used: the Taguchi method, the response surface methodology (RSM), and the non-dominated sorting genetic algorithm II (NSGA-II). The Taguchi method identified the most influential parameter for surface roughness (f > d > s) and for material removal rate (d > f > s). RSM regression models achieved high R2 values of 0.9896 for roughness and 0.9997 for material removal rate. NSGA-II multi-objective optimisation produced 35 Pareto solutions within ranges of cutting parameters, resulting in surface roughness values from 0.239 to 3.301 ?m and material removal rates from 151.53 to 594.99 mm3/s. Confirmation experiments validated the optimal values, with deviations within 10%, confirming the accuracy of the research method for solving the optimisation problem.
  Keywords: multi-objective optimisation; 304 stainless steel; Taguchi method; response surface methodology; RSM; NSGA-II.
  DOI: 10.1504/IJISE.2024.10062971
   
  
• Reliability Analysis of Bleaching System in a Paper Industry  
  by Seema Sharma, Mamta . 
  Abstract: This paper presents a fuzzy technique to examine the reliability of bleaching system in a paper industry using uncertain data. The uncertainties in failure/repair data of every subsystem/component of the bleaching system are quantified using two types of fuzzy numbers, trapezoidal and triangular fuzzy numbers. The basic arrangement of components/subsystems of bleaching system is represented using Petri net model. The fuzzy values of various reliability metrics of bleaching system for different uncertainty levels have been evaluated employing fuzzy - technique. Subsequently, to analyse the failure behaviour of bleaching system and to plan for suitable maintenance policies, these fuzzy values have been defuzzified using centre of area method. The analysis is useful for plant managers to improve the performance of bleaching system by establishing and implementing appropriate maintenance strategies and policies.
  Keywords: reliability analysis; uncertain data; Petri net; fuzzy methodology; trapezoidal fuzzy number.
  DOI: 10.1504/IJISE.2024.10063077
   
  
• Integration of Kansei Engineering and Artificial Neural Network Towards the Implementation of Intelligent Food Packaging Design Based on Consumer Preferences  
  by Sakir Sakir, Bambang Dwi Argo, Yusuf Hendrawan, Sugiono Sugiono 
  Abstract: Packaging design innovation is one of the crucial strategies for consumer-oriented product development. Therefore, this research aimed to design intelligent food packaging (IFP) for beef products using an integrated approach of Kansei engineering (KE) and artificial neural network (ANN) based on consumer preferences. The results showed 37 valid and reliable Kansei words based on Kaiser-Meyer-Olkin measure (KMO), Bartlett’s test of sphericity, and measure of sampling adequacy (MSA) using SPSS 26 software. Based on the results, the best ANN structure was achieved with the Traingd learning algorithm which had 418 inputs, 20 nodes in the hidden layer, and eight outputs with a training mean square error (MSE) of 0.0099991, a validation MSE of 0.0321, a training regression (R) of 0.99287, and a validation R of 0.98928. Therefore, the best IFP design for beef products based on consumer preferences could be achieved by integrating KE and ANN methods.
  Keywords: Kansei engineering; artificial neural network; intelligent food packaging design; consumer preferences.
  DOI: 10.1504/IJISE.2023.10063165
   
  
• A Hybrid Optimisation Strategy for Large-Scale Vehicle Routing Problems with Time Windows using Solution Initialisation  
  by Yongzhong Wu, Minqi Xu, Mianmian Huang 
  Abstract: This paper investigates a novel hybrid optimisation strategy that integrates a machine learning algorithm with a meta-heuristics to tackle large-scale vehicle routing problems with time windows (VRPTW). Specifically, the K-means clustering algorithm is employed to generate initial routing solutions, subsequently optimised by an artificial bee colony (ABC) algorithm. The new approach is tested on large-scale real-life cases. The computational results show that the new algorithm outperforms a well-established ABC algorithm in terms of both objective value and computation time. In addition, the experiments highlight the importance of considering both the distance between customers and customer time windows in the clustering process to ensure good computational results.
  Keywords: vehicle routing problem with time windows; clustering; artificial bee colony algorithm.
  DOI: 10.1504/IJISE.2024.10063184
   
  
• Designing and Assessing Cognitive Training Application for Seniors with MCI: Comprehensive Evaluation Methodology  
  by Dong Zhang, Yazhen Lan, Shan Hu 
  Abstract: This study addresses the absence of design standards for cognitive training apps catering to seniors with mild cognitive impairment (MCI). Integrating qualitative and quantitative methods, it employs grounded theory (GT) for synthesising user interview data and iteratively refines design criteria through theoretical coding. The fuzzy analytic hierarchy process (FAHP) and criteria importance through intercriteria correlation (CRITIC) objectively establish weights for design evaluation criteria and finalise the weighting values using the combined ideal point assignment method. The VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is used to evaluate and select design solutions. This comprehensive approach minimises subjectivity and bias in criteria determination and weighting, enhancing the Objectivity and accuracy of cognitive training application evaluations. Usability questionnaires and user testing validate that the integrated approach improves design decisions' objectivity and scientificity, making the application more responsive to user needs.
  Keywords: healthy ageing; cognitive training; mild cognitive impairment; MCI; grounded theory; evaluation methodology.
  DOI: 10.1504/IJISE.2024.10063243
   
  
• Variant-Rich New Energy Vehicle Powertrain Functional Safety Engineering  
  by Roland Mader 
  Abstract: New energy vehicles (NEVs) are a success story that has led to the continuous introduction of manifold vehicle models and variants. Due to the fact that NEV powertrains are controlled by E/E (electrical and/or electronic) systems, faults and failures of these systems can lead to hazards. In order to prevent such hazards, functional safety engineering aims at avoiding or mitigating safety-critical E/E system faults and failures on the system, hardware and software level. Accordingly, numerous functional safety activities need to be executed and many workproducts are required. Variability of NEVs, high requirements as to achieve functional safety, short development cycles and resource constraints impose challenges for automotive companies. Motivated by an industrial case study we identify variation points of NEV powertrains and analyse their dependencies on the functional safety lifecycle. Based on that, we identify organisational and technical measures which support variant-rich new energy vehicle powertrain functional safety engineering.
  Keywords: Functional Safety; New Energy Vehicle; Battery Electric Vehicle; Hybrid Electric Vehicle; Powertrain; E/E System; Product Line; Variability.
  DOI: 10.1504/IJISE.2023.10063252
   
  
• Optimal operation of microgrid systems considering user energy storage behavior  
  by Junxiang Li, Chenmin Gong, Deqiang Qu, Xi Wang 
  Abstract: This paper establishes a microgrid system optimisation model based on carbon capture and shared energy storage to promote new energy consumption and better reduce carbon emissions. Considering the user's psychology and demand response, the energy storage behaviour of users is analysed to maximise the benefit of energy storage and achieve a win-win situation for both load aggregators and shared energy storage providers. The pricing and energy supply strategies of microgrid operators are optimised based on carbon capture technology. A case study is conducted on a microgrid system located in a business park in southern China. Through numerical simulation and comparative analysis of multiple scenes, it is verified that carbon capture devices and shared energy storage can significantly reduce the microgrid's carbon emissions and the uncertainty of the system operation and improve users'; demand response capability, which is beneficial to the long-term development of the microgrid system.
  Keywords: carbon capture; shared energy storage; demand response; user psychology; energy storage behaviour.
  DOI: 10.1504/IJISE.2024.10063438
   
  
• Effects of building parameters on mechanical and surface properties of 3D printed bioplastic (PLA) using TOPSIS: An experimental study  
  by Senthil S. M, Bhuvanesh Kumar M, Rajeshkumar L, Sampada Viraj Dravid 
  Abstract: Additive manufacturing is becoming an emerging technology in manufacturing three dimensional (3D) components in a layer-by-layer printing fashion. The technology enables wide variety of materials to be printed for different applications starting from automotive, aerospace, marine and to the biomedical fields. 3D printing processes are majorly used to print polymeric materials compared to metallic materials. Polylactic acid (PLA) is the most commonly used material by the fused deposition modelling technique, which accounts for multiple applications. The present study investigates the effect of printing parameters such as infill density, wall thickness, printing speed and ironing to identify optimum process parameter combination for better mechanical performance. Based on the design of experiments, 40 samples were printed and measured for mechanical characteristics. Upon the analysis using TOPSIS, at the parameter combination of infill density (99%), wall thickness (3 mm), and printing speed (150 mm/hr), the printed specimens showed higher tensile strength of 10.20 MPa and comparatively good surface finish. Hence the parameter optimisation showed a positive influence on enhancing the mechanical properties of printed components.
  Keywords: 3D printing; additive manufacturing; polylactic acid; PLA; parameter optimisation; TOPSIS.
  DOI: 10.1504/IJISE.2024.10063451
   
  
• A Study on the Application of Emotional Factors in Medical Products for Paediatric Asthma  
  by Jun Wang, Yazhen Lan, Dong Zhang, Mengqing Liu 
  Abstract: Emotional factors play a pivotal role in enhancing the user experience of medical products for paediatric asthma, mitigating children's negative emotions, and improving treatment adherence. However, a comprehensive framework and assessment criteria for integrating emotional factors into the design of such medical products are lacking. To standardise these criteria, this study integrates emotional design theory, grounded theory, and the fuzzy analytic hierarchy process (FAHP). Firstly, based on the three-level theory of emotional design, an interview outline was constructed to conduct semi-structured interviews with the target users, and the interview content was coded and analysed using the grounded theory to refine the emotional factor design indicators. Subsequently, FAHP assigned weights to these indicators, constructing a comprehensive emotional factor application model. PSSUQ validated product usability, revealing that the proposed model effectively meets users’ emotional needs, thereby standardising emotional factors application criteria and offering theoretical references for designing medical products for paediatric asthma.
  Keywords: emotional factors; emotional design theory; grounded theory; fuzzy analytic hierarchy process; FAHP; paediatric asthma medical products.
  DOI: 10.1504/IJISE.2024.10063622
   
  
• Lean Manufacturing applied in developing countries: a case study in metal industry  
  by Abror Hoshimov, Anna C. Cagliano, Jamshid Inoyatkhodjaev, Antonio Carlin 
  Abstract: This paper proposes a structured approach integrating the main Lean Manufacturing tools to improve the operational performances of companies in developing countries, particularly in Central Asia. A case study approach is applied to a manufacturing company in Uzbekistan. Four steps are performed: selecting a case company; creating a working team and defining the current process mapping; selecting the relevant key performance indicators and Lean tools; implementing Lean tools and assessing improvements. The overall daily production rate of the case company increased by about 28% after Lean application. Developing countries need specific efforts to overcome the barriers related to the cultural background of companies and mentality of their employees, in order to support Lean Manufacturing diffusion. This case study can stimulate academicians focusing on further research about the application of Lean tools in developing countries and the key contextual drivers of its successful implementation.
  Keywords: Lean Manufacturing; Value Stream Mapping; 5S; Developing Countries; Case study; Uzbekistan.
  DOI: 10.1504/IJISE.2024.10063624
   
  
• Reliable Location Models with Transportation Mode Selection (RLM-TMS)  
  by Leena Hamdan, Abdulrahman Alenezi 
  Abstract: In this study, we examined a three-echelon supply chain network consisting of candidate facilities, hubs, and customers. Products are delivered from facilities to customers directly or via hubs, utilising different transportation modes. Full truckload shipments are employed for delivering products from facilities to customers or hubs, while less than truckload shipments are utilised for products from hubs to customers. Considering the susceptibility of facilities to failure, customers are allocated to either a primary facility exclusively or both primary and secondary facilities, with the secondary facility serving customers only during the failure period. The problem is mathematically formulated as a linear integer programming model and subsequently solved using the Lagrange relaxation procedure. The solution was obtained with an average computational time of 96.6 seconds, demonstrating a gap between upper and lower bounds averaging 0.158%.
  Keywords: supply chain management; SCM; reliability; transportation mode; Lagrange relaxation; facility location models.
  DOI: 10.1504/IJISE.2024.10063710
   
  
• Quantifying the Influence of Future Disruptive Scenarios to Priorities of Energy Supply Chains Systems of Liquified Petroleum Gas  
  by Ayedh Almutairi, Fatmah Alfaqeeh, Zachary Collier, James H. Lambert 
  Abstract: The availability of liquified petroleum gas (LPG) is especially critical for commercial and residential users. A risk assessment framework has been established to track risk scenarios and to address the level of disruptions on the priority orders of initiatives under the influences of scenarios. It demonstrated in an LPG facility in the State of Kuwait with 17 emergent conditions, 3 scenarios, 15 initiatives, and 4 evaluation criteria. The shutting down of one centre and the shutting down of two centres were the most disruptive scenarios. The highest ranked initiatives are: Increasing the number of workers for inspection, having random weekly inspections, and having monthly maintenance, respectively. However, these initiatives are the least robust to disruptive scenarios' impact. The initiative, raising customers' awareness of how to safely use LPG cylinders, has the least priority ranking order but is the most robust initiative to the influence of scenarios.
  Keywords: energy supply; risk analysis; emergent conditions; scenario-based preference model; liquified petroleum gas system.
  DOI: 10.1504/IJISE.2024.10063712
   
  
• Challenges & Issues Faced By Pharmaceutical Companies From Supply Chain Management Perspective : A Systematic Literature Review  
  by Prateek Khublani, Anil Bhat, Jyoti Tikoria 
  Abstract: The aim of this study is to examine issues and challenges faced by pharmaceutical companies from a supply chain management perspective. The study employs a systematic literature review approach, complemented by the strategic utilisation of the PRISMA framework, to curate and analyse research papers spanning the years 2013 to 2024. This research sheds light on the prevailing obstacles and evolving dynamics within the sector. The research paper discusses and analyses ten themes related to the selected research topic, considering their connection with identified issues and challenges. The themes derived from this comprehensive analysis, highlight the challenges faced by these pharmaceutical companies. Furthermore, the findings of this research contribute to documenting best practices, enriches the academic discourse and offer a valuable resource for researchers and practitioners seeking to navigate the intricate landscape of pharmaceutical supply chain management.
  Keywords: supply chain management; SCM; supply chain effectiveness; supply chain efficiency; pharmaceutical industry; pharmaceutical supply chain.
  DOI: 10.1504/IJISE.2024.10063965
   
  
• Hybrid optimisation strategy-based economic emission dispatch for microgrid  
  by Nitin Goel, Naresh Yadav 
  Abstract: CEED solution is the procedure of dividing up the required demand of power among the possible producing units while taking into account low fuel costs, decreased emissions, and minimal transmission loss. The multiple objective functions are formulated to single CEED constraint to solve using an efficient algorithm. By combining the peculiar preying characteristics of Harris Hawk and the intellectual food storage characteristics of crow, a novel MIHHO Algorithm is designed to handle the CEED constraint. The efficiency of the optimisation strategy is evaluated with six test cases. The minimised results of transmission loss, economic cost and emission cost for the DG system by MIHHO technique is evaluated over the traditional strategies, such as GA, GWO, WOA, CSA and HHOA. From the outcomes, it is evident that the proposed MIHHO algorithm provides better solution as compared over the existing methods.
  Keywords: wind; solar; microgrids; CEED; optimisation.
  DOI: 10.1504/IJISE.2023.10063968
   
  
• A Multimedia based Patterns Retrieval from Database Patterns and Storing  
  by Vilas Baburao Khedekar, Dharmendra Singh Rajput 
  Abstract: In this work, a novel multimedia Pattern retrieval system is introduced that encapsulates three major phases: 1) feature extraction; 2) pattern generation; 3) pattern matching. The SURF features have been extracted from the audio input and text input. In addition, the video input is converted into RGB to greyscale format, and then the SURF features are extracted from it. The pattern generation phase includes three stages: 1) scaling of features; 2) rules generation with Association rule mining algorithm; 3) optimised rule generation. Initially, the extracted feature is scaled within limits 1 to 20, and the rules are generated for the video, audio, and text signals separately using the Association rule mining algorithm. Moreover, the optimised rules are generated from the extracted rules using the improved GOA model. Then, using the map-reduce framework, the correlation between them is validated.
  Keywords: multi-media; pattern generation; pattern retrieval; apriori algorithm; IGWO.
  DOI: 10.1504/IJISE.2024.10064007
   
  
• Investigation of strategies to generate value from excess obsolete and non-use inventories held at a Locomotive Maintenance Service Organisation  
  by Mahlogonolo Molokoane, Makinde Olasumbo, Kem Ramdass 
  Abstract: Locomotive maintenance organisations play a key role towards ensuring effective repair, overhaul and preservation of locomotives used in a rail car. The locomotive maintenance organisation considered in this study has over the years hold excess obsolete and non-use inventories used for locomotive maintenance owing to poor inventory management practice. Hence, in order to remedy this dilemma, this study investigates strategies that could be deployed to generate value from the excess inventories held in the organisation. Experts' opinion sourced from inventory planners and supplier chain managers, and literature information were used to unveil suitable strategies that could be used to generate value from the excess inventories held in the organisation. The result of this research exercise revealed that lateral transshipment, auctioning, sales to external organisations, repurposing and supplier buy-back are the suitable strategies that could be used to generate value from the excess inventories held in the locomotive maintenance organisation.
  Keywords: locomotive; maintenance; excess inventories; strategies; framework.
  DOI: 10.1504/IJISE.2024.10064009
   
  
• An Exploratory study on CNC machine Technologies, Barriers and Opportunities on adopting Industry 4.0: A Review  
  by Acendino Neto, Fernando Romero, Daisy Damasceno 
  Abstract: Industry 4.0 represents a complete digital transformation in the way companies operate, incorporating advanced technologies. It includes or integrates new technological advances such as additive manufacturing, artificial intelligence, augmented reality, cyber-physical systems, blockchain, cybersecurity and other technologies. While Industry 4.0 and similar technologies offer many conceivable benefits for production, automated machines are essential for driving industries forward. This article proposes an exploratory investigation in the adoption of Industry 4.0 by CNC machine users, focusing on main barriers and opportunities, exploring the challenges faced by these companies and identifying the potential opportunities arising from adoption. It is based on an exploratory systematic review of the literature. The consolidated factors were grouped into categories to help understand the challenges faced by companies in the transition to Industry 4.0. This research identified several barriers for companies using CNC machines, while also highlighting numerous opportunities related to the adoption of Industry 4.0.
  Keywords: computer numerical control; CNC; technology; Industry 4.0; barriers; opportunities; review.
  DOI: 10.1504/IJISE.2024.10064018
   
  
• LOT Streaming Optimisation of Scheduling Problems in Open-Shop Manufacturing Environments  
  by Ammar Al-Bazi, Mahmood Ahmad, Mohammad Shbool, Anees Abu-Monshar, Rami Hikmat Al-Hadeethi, AbdulStattar Al-Alusi 
  Abstract: Scheduling manufacturing operations is vital for companies to thrive under high competition in various manufacturing industries. The production scheduling process allocates resources such as time for each specific operation, detects possible conflicts of allocated resources, controls job release timings on the shop floor, ensures delivery due dates, and thus increases the productivity and efficiency of the workforce. In this paper, a method usually exploited to reduce the production duration, dubbed lot streaming, is adapted and applied to solve scheduling problems in open shop environments. A new integer linear programming (ILP) model is developed to outline the integration of lot streaming scheduling and constraints of partial functionality machines in an open shop environment to minimise the makespan. In such an environment, there are no restrictions on the order in which the machines perform the jobs operations. The developed model is applied and tested on five different hypothetical problems. The experimental results are presented, and the efficiency of the proposed ILP is discussed. It is concluded that considerable reductions in the makespan can be achieved with the inclusion of lot streaming in an open shop production environment.
  Keywords: open shop scheduling; lot streaming technique; mathematical optimisation; partial processing functionality machines.
  DOI: 10.1504/IJISE.2024.10064428
   
  
• Resource Allocation Strategy in Fog Computing  
  by Sharmila Patil (Karpe), Brahmananda S. H 
  Abstract: The idea of fog computing enables the delivery of computational services and resources closer to the endpoints and users, at the network's edge. Due to the large number of devices, determining the best resource allocation in this situation is challenging. Accordingly, a unique resource allocation strategy for fog computing is suggested in this work. The resource allocation of fog computing is made possible by the modeling of a non-linear functionality under the objective function comprising metrics like Service response rate, Execution time, make span, resource consumption, and Reboot rate. The proposed approach also takes consideration for the allocation of resources in urgent scenarios that allow for quick resource distribution. Considering this as the optimization problem, a new optimization model termed as Hybrid Coati Insisted Beluga Whale Optimization (HCIBWO) is introduced in this work. The performance of proposed work is evaluated over the conventional models in terms of different measures.
  Keywords: Resource AllocationFog Computing; Makespan; Execution Time;HCIBWO Model.
  DOI: 10.1504/IJISE.2023.10064429
   
  
• Optimisation of Surface Roughness of FDM Fabricated Parts: Application of Definitive Screening Design and Genetic Algorithm Techniques  
  by V. Chowdary Boppana, Fahraz Ali 
  Abstract: This study presents an experimental investigation on the impact of variations in various fused deposition modelling (FDM) process parameters such as layer thickness, build orientation, raster angle, part raster width, raster to raster air gap, number of contours, contour width and part shrinkage factors on the top surface roughness of FDM printed poly-carbonate parts. To meet the study objective, definitive screening design (DSD) and ANOVA techniques were used to develop a predictive model for establishment of a functional relationship between the selected process parameters and part surface roughness. Thereafter, the predictive model was validated and optimised using genetic algorithm (GA) technique. The comparison of optimal and default process parameter settings showed an improvement in surface roughness of 60.9%. The proposed combined DSD-GA approach can assist practitioners in fabrication of various industrial products to uplift the additive manufacturing (AM) sector.
  Keywords: fused deposition modelling; FDM; surface roughness; poly-carbonate; definitive screening design; DSD; genetic algorithm; GA.
  DOI: 10.1504/IJISE.2024.10064467
   
  
• Towards Development of Measurement Index for Supply Chain Sustainability  
  by Soumyanath Chatterjee 
  Abstract: The subject of sustainability is gaining significance in the study of the supply chain. Sustainability is becoming very important in securing a better future for both the operation of the supply chain and the planet. This paper focuses on developing a supply chain sustainability measurement system by exploring fundamental properties of the supply chain sustainability index and devising methods to assess sustainability at various levels. To gain a comprehensive global perspective, the paper employs the multi-regional input-output model to analyse the supply chain's impact beyond its immediate geography. Ultimately, the paper proposes a standardized and generic supply chain sustainability index that can facilitate comparisons across different supply chains.
  Keywords: Sustainability Index; Supply Chain Management (SCM); Spatial performance measurement; Multi-Regional Input-Output (MRIO); Life cycle assessment (LCA).
  DOI: 10.1504/IJISE.2024.10064469
   
  
• Analysis of Various Image Based Steganography Techniques Busing Different Images  
  by Abhijit Shankar Mali, Manoj M. Dongre 
  Abstract: A detailed survey is elaborated in this paper for classification of optimisation algorithms utilised for image steganography. The reviews are gathered from 50 research papers and methodologies are classified depending on algorithms like cryptography, deep model, LSB, transform, edge detector, sparse, patch and quantum-based algorithms. The analysis is performed using the classification algorithms, evaluation metrics, tool, dataset used, and publication year. From analysis, it is proven that LSB is the category of algorithm is the widely used algorithm for image steganography. Similarly, MATLAB is the most frequently used implementation tool in most of the research papers, and the evaluation metrics, like PSNR, SSIM, and MSE are widely employed in classification algorithms. The research papers that are mostly taken for this survey are in 2020.
  Keywords: wireless communication; steganography; cryptography; image security; authentication.
  DOI: 10.1504/IJISE.2024.10064477
   
  
• Performance assessment of a potential maintenance strategy for legacy avionic systems  
  by Daniel Chitima, Makinde Olasumbo, Kemlall Ramsaroop Ramdass 
  Abstract: This study presents an approach that could be used to appraise the performance of a potential maintenance strategy tailored to maintain legacy avionic systems. A potential maintenance strategy for legacy avionic systems with the appropriate metrics to ascertain its performance, supportability and the required life cycle cost associated with the usage of this maintenance strategy was presented. Avionic subsystems operational and failure data for a period of ten years, literature information and experts' opinions on the lifecycle cost and supportability requirements for the potential avionic systems maintenance strategy were analysed to ascertain the veracity of deploying this maintenance solution. This study revealed that the potential maintenance strategy earmarked for avionic system maintenance, is expected to have a mean time between failure, operational availability, mean time to repair, lifecycle cost and logistical supportability index of 53.4 hours, 0.92, 1.06 hours, $1,219,029.55 and 59 respectively.
  Keywords: legacy avionic system; maintenance; reliability; maintainability; life cycle cost.
  DOI: 10.1504/IJISE.2024.10064554
   
  
• Improving the Competitiveness of the Manufacturing Industry using Mass Customization  
  by Mehari Bezuneh, Bereket Haile, Assefa Tsegaw, Teshome Bogale, Matthias Brossog, Jörg Franke 
  Abstract: Globalisation, market uncertainty, changing customer interests, and shorter product life cycles pose computational challenges to the manufacturing industry. Mass customisation (MC) has emerged as a solution to tackle these challenges by offering customised products while maintaining product cost, quality, volume, variety, and delivery time, which are called competitive factors in the manufacturing industry. However, the effectiveness of the MC strategy depends on how effectively the industry applies different manufacturing systems. Therefore, the main objectives of this research were to identify and determine how the manufacturing system enhances MC capabilities and contributes to the competitiveness of the manufacturing industry. The process involved formulating hypotheses after reviewing existing literature and gathering expert opinions and analyses using the algorithms of the fuzzy Delphi method. Ultimately, this study identified the basic manufacturing systems that can increase MC capabilities, which contributes to improving the competitiveness of the manufacturing industry.
  Keywords: mass customisation; customisation capabilities; sustainable competitiveness; manufacturing systems; enabling factors; fuzzy Delphi method.
  DOI: 10.1504/IJISE.2024.10064556
   
  
• Performance analysis on cluster head selection approaches for WSN-IoT  
  by Ramya R., Brindha T 
  Abstract: This paper presents driven by numerous optimisation approaches for selection of cluster heads (CHs) in WSN-assisted IoT. The process starts with the simulation of IoT nodes during configuration. Moreover, this paper analyses and justifies various cluster head selection (CHS) techniques. Here, the comparative analysis is done by comparing the performance of several optimisation models developed for CHS. Also, the performances of the approaches are calculated with several measures, like energy, LLT, trust, QoS and Throughput. Here, the experimentation was analysed by comparing approaches, like Glowworm swarm with FruitFly Algorithm (FGF), fitness averaged-rider optimisation algorithm (FA-ROA), improved sunflower optimisation algorithm (ISFO), fuzzy-based energy-efficient CHS algorithm (FEECS), and particle-water wave optimisation (P-WWO) for CHS in WSN-IoT. The overall analysis states that the P-WWO model performed better than other models, with values of 0.927 for energy, 0.492 for LLT, 0.934 for trust, 0.796 for QoS, and 0.923 for throughput.
  Keywords: WSN; IoT; energy efficiency; trust; LLT; throughput.
  DOI: 10.1504/IJISE.2024.10064557
   
  
• The role of employee competencies in shaping organisational efficiency: perceptions from five-star hotels  
  by Himani Arora, P. B. Narendra Kiran, Sunil Kumar 
  Abstract: In five-star hotels, achieving optimal organisational efficiency is paramount, with employee competence playing a pivotal role in effective task execution and service delivery. This study explores the significance of employee competencies within this context, rooted in the competency-based view theory. It examines both direct and indirect impacts of employee competency, with knowledge competency mediating and job competency moderating their influence on organisational efficiency. Utilising a sample of 400 employees from various hotel departments, the study reveals a substantial relationship between employee competencies and organisational efficiency. These findings not only validate the CBV theory but also offer practical implications for continuous employee development to enhance efficiency in five-star hotels. The research contributes to theoretical advancements and provides the perceptions for hotel managers and human resource professionals seeking to optimise employee competencies and drive organisational success in the competitive hospitality industry landscape.
  Keywords: organisational efficiency; employee competency; knowledge competency; job competency; five-star hotel.
  DOI: 10.1504/IJISE.2024.10064612
   
  
• Blockchain-based authentication model for education data storage  
  by Basant Kumar  
  Abstract: In this work, a blockchain-based data-sharing model with secured data storage in educational institutions (BDS with SDSE) is presented that integrates storage servers, blockchain, and cryptography approaches to make a secure and reliable environment. Here, blockchain technology is utilised to ensure the reliability and security of data storage. The proposed model comprises four entities, namely education institutions, blockchain, certificate authority, and data centres. The proposed model combines the storage and sharing of educational records among institutions by using blockchain and a data centre. The blockchain ensures the security and auditability of the data, while the data centre is employed to establish record permissions. Finally, the experimentation analysis is performed for proposed model, and it presented enhanced performance with a memory usage of 0.418 MB, detection rate of 0.8 and computation time of 16.913 (s).
  Keywords: blockchain; authentication; education; data storage; key generation.
  DOI: 10.1504/IJISE.2024.10064613
   
  
• Big Data Stream Classification in Apache Spark Platform using Adaptive Dragonfly Moth Search Algorithm  
  by Srivani B, Sandhya N., Padmaja Rani B 
  Abstract: The big data streaming is done using two phases, like offline and online, which is carried out based on the proposed optimisation algorithm, named adaptive-DMS algorithm. In the offline phase, the input text data is initially classified as sub-sets and provided as the input to individual slave nodes. In the slave nodes, the pre-processing is done to remove the unwanted data present in the input using stop word removal and stemming. After pre-processing, TF-IDF is applied for extracting the best features and then classification is done. The same process is repeated for online phase. The error is determined based on the resulted features obtained from online phase and offline phase. If the error is maximal, the final classified data is determined by remodel the classifier by setting the boundary weights.
  Keywords: big data classification; Apache Spark; TF-IDF; stemming; stop word removal; deep RNN.
  DOI: 10.1504/IJISE.2024.10064713
   
  
• Integrated Squid Game with Coati Optimisation Algorithm for Resource Allocation System for NOMA System in Industrial Internet of Things  
  by Kapil Netaji Vhatkar  
  Abstract: The development of IIoT is the scarcity of spectrum resources. It consumes a significant amount of energy while increases the system's spectrum effectiveness. This paper shows the resource distribution in Non-Orthogonal Multiple Access models for IIoT applications from the view of power efficiency. In this paper, the hybrid optimization is used for reducing the energy consumption of power resources and channel resources. An Integrated Squid Game with Coati Optimization Algorithm (ISG-COA) is developed by integrating Squid Game Optimizer (SGO) and Coati Optimization Algorithm (COA) for resource allocation in IIoT scenarios. The limitation of user service quality criteria is also added to the existing optimization problem in order to prevent the situation where the data transmission quality is substantially impaired as a result of the system's energy-saving measures. The algorithm's average system energy efficiency is higher according to the strategy performance simulation experiment when compared to traditional resource allocation algorithms.
  Keywords: Energy-Efficient Resource Allocation System; Integrated Squid Game with Coati Optimization Algorithm; Industrial Internet of Things.
  DOI: 10.1504/IJISE.2024.10064721
   
  
• A Part-Mix Batch-Sizing and Machinability Data System for Milling Operations: An Optimal Sustainable Cost of Quality Approach  
  by Abdulnasser El-Gaddar, Ahmed Azab, Mohammed Fazle Baki 
  Abstract: With increased global competition and higher demand for sustainability in emerging markets, manufacturers are actively exploring new avenues to reduce production costs without compromising product quality. To address this challenge, a novel mixed integer nonlinear model is formulated by incorporating internal quality costs, environmental impact considerations, and the impact of buffer size, to solve the micro-Computer Aided Process Planning problem. Scope covered is limited to milling operations for a part mix involving different materials being machined. Surface roughness is used to evaluate the desired quality level of finish. The internal quality failure cost model, including scrap and rework, is developed based on Taguchi's quadratic loss function. Mathematical programming is employed to validate the results of Genetic Algorithms (GAs). Because of the nonlinear nature of the model, GAs has been used. Considering strict quality cost measures, the model minimizes internal quality-related costs while minimizing the environmental impact.
  Keywords: Keywords: Micro Computer Aided Process Planning; Machining Parameters; Internal Failure Cost; Buffer Size; Genetic Algorithms; Mathematical Programming.
  DOI: 10.1504/IJISE.2024.10064795
   
  
• Double Parametric Scheme Based Multi-objective Student Project Assignment Problem by Fuzzy Programming Technique with Linear Membership Function  
  by Sunil Bhoi, Jayesh Dhodiya 
  Abstract: This paper presents the mathematical model of multi-objective student project allocation problem based on double parametric form of fuzzy preferences and its solution by fuzzy programming technique with linear membership function. Fuzzy preferences are utilised due to fuzzy nature of internal assessment of students by supervisors and feedback analysis of supervisors provided by students. Double parametric scheme is applied to transform this fuzzy model into crisp model and then crisp model is solved by fuzzy programming technique with linear membership functions for different values of and . The results are obtained using LINGO software. The model providing the efficient solutions which can be used by decision maker to allocate projects to students with better degree of satisfaction for students and supervisors. To check the strength and efficiency of proposed model, numerical data-based case is studied and results are discussed.
  Keywords: student project assignment; multi-objective optimization;0-1 integer programming; double parametric scheme; fuzzy programmingor technique.
  DOI: 10.1504/IJISE.2024.10064949
   
  
• Modularity-Based Mass Customization for the Competitiveness of the Manufacturing Industry  
  by Mehari Bezuneh, Assefa Tsegaw, Bereket Haile, Teshome Bogale, Matthias Brossog, Jörg Franke 
  Abstract: The manufacturing industry is undergoing a significant transformation into mass customisation (MC) to achieve sustainable competitiveness. The effectiveness of MC strategy pivots on a firm's ability to achieve product variety with high volume, low costs, superior quality, and fast delivery. Modularity-based manufacturing approach (MBMA) is utilised as one strategy for the effectiveness of MC capabilities. However, the impact of MBMA on each MC capabilities is not adequately addressed. Hence, this study examines how MBMA impacts each MC capabilities, thereby enhancing competitiveness of the manufacturing industry. Hypotheses were developed following a thorough review of the literature on manufacturing capabilities and modularity-based manufacturing. Expert opinions were collected using structured data-collection tools, and analysis was conducted using the algorithms of the fuzzy Delphi method. The research findings indicate that applying an MBMA in MC strategy enhances MC capabilities and ultimately contributes to increasing the competitiveness of the manufacturing industry.
  Keywords: mass customisation capabilities; modularity based manufacturing; manufacturing competitiveness; fuzzy Delphi method.
  DOI: 10.1504/IJISE.2024.10065047
   
  
• Smart Urban Metamorphosis: Revolutionising through Synergized Lean Supply Chains and IoT Integration: A Case Study  
  by Mohammad Reza Gharib, Najmeh Jamali, Behzad Omidi Koma, Farzaneh Ghasemi 
  Abstract: This study addresses challenges in traditional supply chains by advocating a shift towards intelligent, resilient systems. It emphasises integrating the internet of things (IoT) into supply chain management, proposing a secure infrastructure connecting data, goods, and all supply chain activities. In smart cities, aligning with urban modernisation goals, these efforts optimise business practices. The research explores agile technological methods for developing IoT solutions in sustainable urban environments, aiming for a platform that supports rapid expansion and addresses economic, social, cultural, and transportation challenges. Using a descriptive-analytical approach, the study anticipates transformative changes in smart cities due to rapid urbanisation. It investigates the impact of lean supply chains (LSC) on smart cities, presenting model-based solutions. Isfahan serves as a case study, revealing the benefits of integrating LSC within the smart city framework. This research contributes to innovative urban solutions, emphasising the convergence of LSC and IoT for sustainable, resilient cities.
  Keywords: internet of things; IoT; lean supply chain; LSC; smart city; manufacturing; transportation; tourism.
  DOI: 10.1504/IJISE.2024.10065135
   
  
• Arithmetic Adopted Chimp Optimisation Algorithm for Optimal MPA Design  
  by Meena Chavan, B. Bhuvaneswari, J.V.N. Ramesh 
  Abstract: Presently, MPAs are often used in a number of appliances due to their properties like lightweight, compatibility, reduced volume, low cost, and ease of installation on hard surfaces. Optimal antenna design ensures better results in almost all applications. Hence, this paper aims to introduce a novel Microstrip patch antenna design optimisation that ensures enhanced antenna performance with the optimal design parameters like substrate thickness, width, height, and length, under the satisfaction of multi-objectives like gain, bandwidth, antenna efficiency, return loss, and TARC. To solve the given optimisation problem, this work implements an arithmetic adopted chimp optimisation algorithm (AAChOA) scheme that merges the strategy of the chimp optimisation algorithm (ChOA) and arithmetic optimisation algorithm (AOA) to obtain higher antenna performance. Finally, the proposed technique was evaluated over other extant models based on different parameters like antenna gain, efficiency, total active reflection coefficient (TARC), return loss, and bandwidth.
  Keywords: microstrip patch antenna; MPA; antenna efficiency; return loss; gain; optimisation.
  DOI: 10.1504/IJISE.2024.10065274
   
  
• Redefining Energy Sustainability by Circularising Coal: Evaluating the Challenges for Coal-Fired Thermal Power Plants  
  by Taquiuddin Quazi, Vivek Sunnapwar, Nilesh Ghongade 
  Abstract: Coal-fired thermal power plants must implement circular economy practices to overcome environmental issues associated with their emissions. There are various challenges in the path of the adoption of the circular economy concept. This study examines the challenges in the multi-criteria environment to explore the cause-and-effect relationships between them. Also, the challenges were ranked based on their significance. The findings of the study highlighted causal factors, namely regulatory difficulties, technological challenges, and energy efficiency as the significant ones. Whereas cultural shift and socio-economic factors were found to be dependent factors. The results also offer implications for academicians, researchers, and practitioners. Adopting circularity concepts helps to reduce the negative impact on the ecosystem and creates jobs which addresses the social dimension of sustainability.
  Keywords: circular economy; coal-fired thermal power plant; sustainability; barriers; environmental performance.
  DOI: 10.1504/IJISE.2024.10065319
   
  
• Optimizing Vehicle Safety: Fuzzy Logic-Controlled Automatic Braking Systems  
  by Monika Dhiman, Pratima Manhas 
  Abstract: The alarming rise in road accidents underscores the critical need for effective vehicle brake systems. Vehicles lacking such systems are highly susceptible to accidents, resulting in devastating consequences. Human errors during driving, including delayed reaction times and distractions, contribute significantly to these incidents. An automatic braking system serves as a vital solution to maintain vehicle stability, prevent wheel lock, and avert collisions with obstacles. This study aims to achieve several objectives: design an ultrasonic sensor-based obstacle detection model, create a model for an antilock braking system (ABS). Results from the simulations indicate a substantial 22 percentage improvement in braking torque. The stopping time is improved by 30% by using fuzzy controller in place of PI controller. Consequently, this improvement results in notably shorter stopping times and distances compared to standard PID control, signifying promising advancements in vehicle safety.
  Keywords: slip ratio; fuzzy logic; ultrasonic sensor; model; antilock braking system; ABS; wheel lock.
  DOI: 10.1504/IJISE.2024.10065627
   
  
• Efficient Algorithms for Cellular Manufacturing Systems with Deterioration Effect and Inventory (Case Study: Stone Paper Factory)  
  by Mostafa Jafari, Amir Hossein Akbari 
  Abstract: This research investigated the order acceptance and scheduling problem in CMS with tardiness cost, the deterioration effects of machines, and the cost of ordering and holding raw materials. Received orders have revenue, processing time, due date, tardiness cost, and decisions about acceptance or rejection are made. We have proposed a Linear programming mathematical modeling with objective profit maximization. Due to the NP-hard nature of the problem, A meta-heuristic algorithm based on a genetic algorithm is introduced to solve large dimensions. The proposed model has been tested in 4 modes: CODH, COFH (removal of the deterioration effect of the machine), CODI (the removal of holding and ordering raw), and CSDH (removal of acceptance and scheduling of all orders). The results show the performance of the proposed algorithm. The authors’ findings help managers to make better decisions, improve supply chain actions, enhance competitive advantage, enhance customer satisfaction and attract more customers.
  Keywords: Cellular manufacturing system; Order acceptance and scheduling; Deterioration effect; Meta-Heuristic Algorithm; holding and ordering; stone paper.
  DOI: 10.1504/IJISE.2024.10065676
   
  
• Modelling of disturbances and their influence on physical systems  
  by Tabassum Rasul, Koena Mukherjee 
  Abstract: The effect of disturbance is a common phenomenon in any physical system. Although the nature of the disturbance can vary from system to system, the effect remains the same and leads to errors in measurement as well as control of the system. Thus, to control a system effectively, sufficient study on disturbance modelling is required. The paper reviews pertinent literature on the effects of disturbances in various systems. Different signals are used to model disturbances in different systems. This paper explores and discusses these characteristic signals and equations used to describe disturbances in different systems. The study also includes a survey of the efficient control techniques offered in relevant literature to limit the effects of disturbances. Furthermore, the effectiveness of disturbance observers in disturbance attenuation is demonstrated via simulation using MATLAB/ SIMULINK.
  Keywords: disturbance; Continuous Stirred Tank Reactor (CSTR); electric grid; hydro-servomotor; manipulator; Disturbance Observer (DOB).
  DOI: 10.1504/IJISE.2024.10065812
   
  
• Student Academic Performance Prediction with MapReduce using the Optimized Deep Belief Network  
  by Kamakshamma V, Bharati K.F 
  Abstract: The emergence of learners count in the e-learning infrastructures has inspired the researchers to carry out the data driven learning assessment for the learning performance enhancement amidst the students. A new technique, namely Chicken Squirrel search algorithm-based deep belief network (CSSA-DBN) is devised with MapReduce for predicting the student academic performance. Here, the details of students are attained, which performs pre-processing with log transformation for making it suitable for improved processing. Moreover, the mappers perform selection of feature with correlation-based Tversky index wherein Pearson correlation coefficient and Tversky index are integrated to choose imperative feature. In addition, the reducers perform student performance prediction with Deep belief network (DBN), trained using the proposed Chicken Squirrel search algorithm (CSSA). Proposed CSSA is devised by blending chicken swarm optimization (CSO) and Squirrel search algorithm (SSA).
  Keywords: Map Reduce; Deep belief network; Log transformation; Student academic performance; Tversky index.
  DOI: 10.1504/IJISE.2024.10065814
   
  
• A Descriptive Study on Impact of Artificial Intelligence Determinants among the Talent Acquisition Management in IT Companies  
  by Debjani Guha, Kirti Mahajan 
  Abstract: This research underwent an empirical investigation to examine the application of AI in the talent acquisition process in software technology parks and IT hubs. The research was primarily focused on the following topics: 1) talent acquisition management; 2) AI support in pre-selection; 3) AI support in telephone interviews; 4) AI support in training and development; 5) AI support in organisational culture; 6) appropriate teams. This is a clear-cut policy that can be examined through this research effort since it plays a significant role. The proposed approach was implemented in two stages. The first step began with the collection of data, which explicitly examined the applications of artificial intelligence technology. The data was collected using a structured questionnaire comprised of 30 questions. The evaluation was carried out for the collected data using regression analysis.
  Keywords: artificial intelligence; talent acquisition; information technology; human resource manager; training and development.
  DOI: 10.1504/IJISE.2024.10065821
   
  
• Evaluation of Ergonomic Risk Factors in Plastic Manufacturing: Case Study  
  by Ahmed Nasser, Mahmoud A. El-Sharief, Mahmoud Heshmat 
  Abstract: Workers in plastic manufacturing facilities are exposed to repetitive tasks and awkward postures due to massive production. This research aims to evaluate ergonomic risk factors in a factory for plastic household cleaning tools. Focusing on the drilling and tufting brush machine as a real case study. Using the Jack Siemens ergonomic simulation and questionnaires, the study found workers experienced pain in their lower back, wrist, and right arm with a significant level of joint angles in the upper limbs and prolonged standing. Furthermore, the workstation layout was redesigned to include human factors, resulting in substantial ergonomic improvements: a 38% decrease in compression force, a 45% reduction in standing time with a 45% increase in sitting time, and an 8% decrease in the time spent at significant and moderate joint angle levels. This reduction signifies substantial improvements in the ergonomic aspects of the workstation.
  Keywords: ergonomics; musculoskeletal disorders; MSDs; Jack Siemens; workstation; tufting brush machine.
  DOI: 10.1504/IJISE.2024.10065898
   
  
• Analysing Customers' Shopping Behaviours: A Case of A Supermarket in Taiwan  
  by Chun-Ju Chang, Ming-Min Lo, Hao-Wei Yang, Hsin-Hung Wu 
  Abstract: In the retail industry, cluster analysis has been commonly used in analysing customer behaviours. This study utilises the database of customer transaction data from a supermarket in Taiwan in the first half of 2021. Twenty-two merchandise items are defined as the most frequently purchased products by member customers. A two-stage cluster analysis is employed. The results show that 8,073 member customers are grouped into ten clusters, with three clusters representing male customers, while the rest are female customers. Cluster 8 with a group of 51 female customers with all age groups is found to be the core customers for this supermarket. Cluster 10 consisting of female customers whose ages are 25 and above is the essential customers. In contrast to female customers, cluster 3 covers all age groups and is the most essential male customers for this supermarket and can be viewed as the core customers as well. The supermarket management can develop different marketing strategies for each cluster to meet member customer needs.
  Keywords: K-means method; self-organising maps; cluster analysis; customer behaviour; transaction data; database; supermarket; Taiwan.
  DOI: 10.1504/IJISE.2024.10065900
   
  
• Exploring the Digital Marketing Strategies that Drive Business Performance: A Focus on Technological Capabilities as a Mediator  
  by Gaurav Kumar Bisen, Sana Ashraf, Priya Yadav, Vipin Vihari Ram Tripathi 
  Abstract: The global business paradigm has been greatly impacted by the emergence of digital marketing, which has supplanted traditional marketing. From a business-level viewpoint, the study provides a framework to describe how the change in digital marketing affects business performance in the new normal setting. Additionally, it examines how technology skills function as a mediator in explaining company success. The cross-sectional study method was employed to collect data from 365 officials of SMEs in India. Following the application of the AMOS using SEM and the linear regression method through SPSS, it was found that online advertising, search engine marketing, and social media networking have a direct impact on business performance. Also, the outcomes indicate that technological capability is a mediator influencing the link between digital marketing and business performance. Finally, based on these findings, several interesting directions for further study as well as implications are proposed.
  Keywords: digital marketing; business performance; technology; social media marketing; business transformation.
  DOI: 10.1504/IJISE.2024.10065901
   
  
• A survey on different Rainfall Forecasting Techniques  
  by Vijalakshmi C, Pushpa M 
  Abstract: Rainfall prediction is critical because it has a variety of causes, such as crop loss and property damage; rainfall forecasting is critical for increasing community resilience and welfare because it has a direct impact on agriculture. There are 50 research papers reviewed that used various rainfall forecasting techniques. The research papers are categorised and reviewed using various techniques. Among them are optimisation-based techniques, ANN-based techniques, DNN-based techniques, fuzzy-based techniques, and ML-based techniques. There is also a list of research gaps and issues identified in previous works. As a result, the researchers can implement a solution and continue their research. The works reviewed in the literature are scrutinised in terms of software tools, datasets, performance evaluation metrics, and the results obtained by those techniques. The review lists the future extent by taking into account the difficulties encountered in rainfall forecasting literary works to improve their works.
  Keywords: rainfall forecasting; deep learning; machine learning; deep neural network; artificial neural network; genetic algorithm; particle swarm optimisation.
  DOI: 10.1504/IJISE.2024.10065909
   
  
• A Literature Review on Role of Multi-Agent Technology in Manufacturing Sectors: A Simple Meta-Analysis  
  by Vikram Singh, Somesh Kumar Sharma 
  Abstract: This review is the first attempt that aims to review the role of MAT in manufacturing organisations. The review examined 892 articles from diverse search engines. After screening, 93 articles published between 20092023 were finalised. Herein, two classification schemes are proposed to arrange the data. After that, a simple meta-analysis is performed. This analysis noticed the least publications in the domains shopfloor production, inventory control, inspection, and quality control, and in research techniques fundamental research (like static and mathematics) and applied research. The review also identified 43 MAT active journals having aim and scopes of MAT research. A fluctuating research trends have been noticed which is increasing in the last three years. Based on these findings, future research is suggested in the areas having least publications. Moreover, this study presents an extensive list of references that may provide a source for readers/researchers/scholars working or interested to explore on MAT research.
  Keywords: agent-based system; literature review; classification schemes; simple meta-analysis; manufacturing sectors.
  DOI: 10.1504/IJISE.2024.10066052
   
  
• Survey on the Research of Various Machine Learning and Deep Learning Techniques for Precipitation Forecasting  
  by Gujanatti Rudrappa, Nataraj Vijapur 
  Abstract: A detailed survey is elaborated in this paper on precipitation forecasting for weather forecasting. The reviews are gathered from 50 research papers and the techniques are classified into three types, such as deep learning (DL), machine learning (ML) and other methodologies. The analysis uses the techniques adopted for precipitation forecasting, publication year, utilised tools, employed dataset, and evaluation metrics. From the analysis, it is proven that the DL-based technique is the highly utilised technique for data protection. The papers that were mostly obtained in 2023 were taken into account for this research. The techniques utilised in most of the research papers were evaluated based on the root mean square error (RMSE), and the most utilised dataset for developing the model in this survey paper is NOAA. Moreover, MATLAB is a frequently employed tool for implementation.
  Keywords: machine learning; deep learning; precipitation forecasting; recurrent neural networks; long short-term memory; LSTM.
  DOI: 10.1504/IJISE.2024.10066069
   
  
• Gateway Selection for Heterogeneous IoT using Dynamic BHFC and Thresholding  
  by Jyoti R. Desai, Annapurna D 
  Abstract: The main aim of this research is to implement an effective internet of things (IoT) gateway selection technique. Initially, the heterogeneous IoT network is simulated where the simulated network consists of IoT nodes, IoT gateway and cluster head (CH). Then, CH selection is performed by dynamic or incremental clustering, which is achieved by adapting black hole entropic fuzzy clustering (BHEFC) with respect to different parameters, namely energy, distance and information entropy. The next step is gateway selection where the process is performed and the ON/OFF strategy is accomplished using hybrid metrics where the parameters, like number of nodes, delay, and link quality considered for gateway selection. Furthermore, optimal routing path is selected by competitive multi-verse dragonfly algorithm (CMVDA). The CMVDA is derived by the integration of the competitive multi-verse optimiser (CMVO) and dragonfly algorithm (DA). Here, the implementation of the method is done by the Python tool. This method achieved performance throughput of 0.470915, energy of 0.638078 J, and delay of 0.399453 sec.
  Keywords: internet of things; IoT; gateway selection; competitive multi-verse optimiser; CMVO; black hole entropic fuzzy clustering; BHEFC; dragonfly algorithm.
  DOI: 10.1504/IJISE.2024.10066130
   
  
• Designing a humanitarian supply chain network in dynamic conditions for transferring of relief items under demand uncertainty  
  by Mehrnaz Bathaee, Arash Apornak, Mohammad Reza Pourhassan 
  Abstract: One of the challenges in crisis situations after the incident is meeting the demands of the victims. The main purpose of this article is to minimize the amount of unmet demand based on the priority of demand points. Due to the computational complexity of the problem, which is Np-hard, a super-innovative algorithm called genetic algorithm was designed to solve the real-world problem in large scale (Kermanshah earthquake in Iran) and finally the efficiency of the model was evaluated through Sensitivity Analysis. In addition, the results obtained from the robust approach compared to the traditional approach showed that in the best and the worst scenario, the unmet demands in the robust approach compared to the traditional approach was respectively 43% and 21% less than the traditional approach, which indicates the efficiency of the robust approach compared to the traditional approach.
  Keywords: relief supply chain; uncertainty; robust optimisation; genetic algorithm.
  DOI: 10.1504/IJISE.2024.10066508
   
  
• A non-basic model: a case of perishable items with different demand locations  
  by Adebayo Tolulope Adedugba 
  Abstract: The study investigated a multi-item tomato production and conveyance framework in which multi-tomato items are delivered at the same time from an extensive variety of resource frameworks. The goal of sustainable production design is to fulfil market needs beyond limitations. This study outlines a mathematical model that reproduces tomatoes into numerous items and moves them to different demand locations. It likewise consolidates a model to further develop production and circulation design simultaneously. The issue was formulated and developed as a mixed integer approach. Then, at that point, the study formulated a methodology of non-basic constructs free of constraints with an integer approach, i.e., non-integer framework. Therefore, the study posited an optimal flexible production echelon.
  Keywords: production; echelon; optimisation; inventory chain; conveyance.
  DOI: 10.1504/IJISE.2024.10066509
   
  
• Chronological White Shark Optimisation_PyramidNet for Tuberculosis Bacilli Segmentation and Infection Level Identification  
  by Gavendra Singh, Faizur Rashid 
  Abstract: The main objective of this research is to identify the infection level or severity of Tuberculosis (TB) by PyramidNet which is trained by Chronological White Shark Optimization (CWSO). TB is usually caused by Mycobacterium TB bacteria, which generally affects the lungs. This also affects other body parts. Most infections of TB show no symptoms, which is known as latent TB Bacteria causing TB are spread while the infected person sneezes or coughs. Weight loss, night sweats, as well as fever are common symptoms of TB. In this work, finding the severity of TB or its infection level is done by DL enabled optimized algorithm. Here, PyramidNet is a DL model, trained by the proposed CWSO for infection level identification. Also, denoising is carried by the median filter in the pre-processing stage and bacilli segmentation is done by SegNet, trained by the proposed CWSO. Moreover, appropriate features extracted are fed for severity identification of TB by PyramidNet. Furthermore, CWSO is the hybridisation of chronological concept along with WSO.
  Keywords: Tuberculosis (TB); White Shark Optimization (WSO); PyramidNet; SegNet; Median filter.
  DOI: 10.1504/IJISE.2024.10066570
   
  
• Unique Welding Slag Breaker System for Improved Occupational Safety and Sustainable Efficiency in the Fabrication of Water Wall Panels for Thermal Boilers  
  by Dinagaran D, Balasubramanian K. R, S.P. Sivapirakasam, Prasanna N, Gopanna Kuruva, Yoganathan R 
  Abstract: The current literature emphasises the use of submerged arc welding in water wall panel fabrication. However, the manual cleaning of welding slag presents challenges, prompting the development of an innovative slag breaker system within the submerged arc welding process. This manuscript describes the welding slag cleaning process, eliminate hazardous manual operations, and reduce environmental impact. The research further addresses the gaps in the literature by proposing an optimised cycle for water wall panel fabrication, integrating the slag breaker system. The manuscript highlights the benefits of reduced cycle time by 55.8%, enhanced occupational safety and minimised environmental concerns, contributing to sustainable manufacturing practices. The novel slag breaker system's design and integration into the submerged arc welding process are detailed, emphasising its practical application. Results demonstrate a streamlined cycle, significantly reducing manual labour, enhancing occupational safety, improving overall operational efficiency and reveals savings of INR 26,36,480/year emphasising the economic advantages.
  Keywords: welding slag; water wall panel; submerged arc welding; SAW; occupational safety; slag cleaning; air pollution; noise pollution.
  DOI: 10.1504/IJISE.2024.10066680
   
  
• An Integrated SEM and Entropy Approach to Analyse the Impact of Multi-Agent Technology on the Quality of High-Tech Products  
  by Hritik Kamta, Vikram Singh, Somesh Kumar Sharma 
  Abstract: The current research contributes to the literature by assessing the role of Multi-Agent Technology (MAT) in the manufacturing of High-tech products. For this, a conceptual model of five factors of high-tech manufacturing and twenty-five variables of MAT (explored from literature) has been constructed. The model is tested through Structure Equation Modelling (SEM) to prioritize and determine the interrelationships among the factors and variables. This evolved High-tech manufacturing process is the highest prioritized factor followed by others while production planning, resource agent, and Intelligent Shop Floor Management based on MAS are the top three variables among others. These outcomes of SEM are cross-examined through Entropy for more accuracy. The research concludes that implication of evolved priority orders in manufacturing organisations may automate systems which can increase quality, and productivity, and reduce maintenance efforts. This research contributes to high-tech manufacturing management and researchers to advance their knowledge and advance their manufacturing capabilities through the strategic application of multi-agent technology.
  Keywords: High-tech product manufacturing; Multi-Agent Technology (MAT); Structure Equation Modelling (SEM); Entropy.
  DOI: 10.1504/IJISE.2024.10066690
   
  
• Cost-Effectiveness Evaluation in Supply Chain Using Network Data Envelopment Analysis  
  by Faranak Hosseinzadeh Saljooghi, Elham Zaker Harofteh 
  Abstract: Economic difficulties such as high inflation , deep recession and austerity are disastrous for any country . Monitoring and analysis of every economic activity is necessary to prevent such a scenario. The cost-effectiveness (CE) analysis yields an optimal evaluation of economic activity, which emphasises using minimum resources. Numerous techniques are employed to assess cost-effectiveness; however, none apply to internal organisational structures regarding the operational performance metrics of a company. Therefore, this paper presents a new method using data envelopment analysis to measure CE in a two-stage network system so that all outputs of the first stage are used as inputs of the second stage to produce final outputs. We have concluded that the units that display cost-effectiveness are cost-efficiency. Finally, an example elaborates applicability and merits of the proposed method.
  Keywords: Cost-Effectiveness Analysis; Supply Chain; Two-stage network systems; ?Network Data Envelopment Analysis.
  DOI: 10.1504/IJISE.2024.10066697
   
  
• Kansei Engineering Modelling of Intelligent Food Packaging for Beef Products Using Artificial Neural Network Optimisation  
  by Sakir Sakir, Bambang Dwi Argo, Yusuf Hendrawan, Sugiono Sugiono 
  Abstract: Prediction is one of tasks in the application of Artificial Neural Network (ANN). The utilization of ANN has recently become widespread for predicting product designs, but most research only used one algorithm to train a small dataset. Therefore, this research aimed to predict the design of Kansei engineering-based Intelligent Food Packaging (IFP) for beef products. The dataset comprised 418 inputs, derived from combinations of 19 Kansei words and 22 categories of packaging design attributes. An ANN model was developed and trained by comparing 11 learning function algorithms. This research addressed the gap in predicting the design of IFP using various ANN training algorithms. The results showed that ANN trained with the Gradient Descent backpropagation algorithm (traingd) provided the highest accuracy. Traingd showed the best fit with the highest R and R2 values as well as the lowest MSE, MAD, and RMSE of 0.9949, 0.98915, 0.0333, 2.1353E-05, and 0.00043656, respectively.
  Keywords: Artificial neural network; Intelligent food packaging design; Kansei engineering; Modeling.
  DOI: 10.1504/IJISE.2024.10066699
   
  
• Handover Mechanism of SDN using Puzzle Spotted Henna Optimisation  
  by Pallavi Sapkale, Vaishali Jadhav, Baban Uttamrao Rindhe, Pandharinath Appasaheb Ghonge, Sanjay Ramkrshnan Sange, Moresh M. Mukhedkar 
  Abstract: In recent years, the vehicular ad hoc network (VANET) has been utilised to facilitate connections between vehicles and everything (V2X) and vehicle-to-vehicle (V2V). The speed of data transfer is impacted by the frequent handoffs between road side units (RSUs). To address such issues, this work develops the puzzle spotted Henna optimisation algorithm (PSHOA) for effectual handover. The satellite, RSU, and software-defined networking (SDN) controllers are presented in the SDN control layer. During the handover procedure, the control layer selects the appropriate candidate RSU using the PSHOA. The vehicle connects to the web server and transmits the data across the pathways when it receives the handover order. The end-to-end delay, handover quality of RSU selection, and throughput metrics are used to assess the performance of the system, in which the ideal values of 0.701 s, 0.922, and 95.08 Mbps are obtained.
  Keywords: puzzle optimisation algorithm; POA; software defined vehicular networks; SDN; spotted Henna optimisation; SHO.
  DOI: 10.1504/IJISE.2024.10066731
   
  
• Hybrid Optimization for early Parkinson's Disease Detection in Federated Learning  
  by Senthilnathan Chidambaranathan, Faizan Ahmad, Vijayakumari Rodda, Roopa Chandrika Rajappan, Venkateswaran R., Sangeetha S 
  Abstract: Recently, a rapidly developing neurodegenerative disorder is Parkinson’s disease (PD) which commonly affects the population of elder persons of individuals over 50 years. There’s been no medication for PD still now. Despite that, in the early stages, detecting PD is challenging. Thus, earlier detection is required to maximise the life of patients. In this research, the major intention is to project FedL_WFSA_ResneXt-DNN for identifying PD detection. Two entities are involved such as nodes and servers, whereas the models that exist in FL are training and global models. By acquiring the input image from the given dataset, PD detection takes place inside the local training model and the input image is pre-processed using a weighted median filter. The essential features are extracted and then, PD detection is performed by ResNeXt-DNN and the hyperparameters tuning is conducted utilising the proposed WFSA.
  Keywords: weighted median filter; ResNeXT; waterwheel plant algorithm; WPA; flamingo search algorithm; FSA; deep neural network; DNN.
  DOI: 10.1504/IJISE.2024.10066737
   
  
• Reliability Analysis for Phased-Mission System of Supply Chain based on BDD  
  by Xueqing Tao, Yongjin Zhang 
  Abstract: Supply chain can be viewed as a network structure of member enterprises, and the evaluation of reliability behaves an important significance for improving the efficiency of management. In this paper, the supply chain is viewed as a phased-mission system (PMS), the reliability of the PMS based on the delivery time of suppliers is investigated. Consider the complexity of network structure in supply chain, the reliability model for each stage in the system is developed based on the fault tree and binary decision diagram (BDD), then the total reliability of the PMS of supply chain is deduced by simplifying the redundant structure of the fault tree, meanwhile, the flow of assessment for reliability at certain stage is also given. Finally, a numerical example is presented to validate the proposed approach. The results should be useful for evaluating the reliability and safety of a supply chain environment.
  Keywords: reliability analysis; phased-mission system; PMS; supply chain reliability; binary decision diagram; BDD; network structure.
  DOI: 10.1504/IJISE.2024.10066739
   
  
• Augmented Reality and Virtual Reality in Product Customisation: Effects on Consumer Decision-making and Satisfaction Levels  
  by Vipin Vihari Ram Tripathi, Ankita Kumari, Pavnesh Kumar, Gaurav Kumar Bisen, Thakur Digbijay Singh, Rukmani Jaiswal 
  Abstract: In this research work, an empirical study is undertaken to gather information about the factors influencing the VR/AR technologies on consumer purchase intention of e-shopping users and also about the attitude of online customers impact the purchase intention of buyers in India. It seeks to explore the specific ways in which these immersive technologies alter traditional consumer decision-making processes. A questionnaire has been prepared with 20 questions, under three categories: 1) consumer buying behaviour, 2) attitude towards online shopping, and 3) augmented reality and virtual reality. The prepared questionnaire was distributed among online consumers in India, and the responses based on their perspectives have been collected. The collected responses were analysed via exploratory and confirmatory factor analysis and SEM analysis.
  Keywords: augmented reality; AR; consumer buying behaviour; CBB; digital transformation; technology; virtual reality; VR.
  DOI: 10.1504/IJISE.2024.10066919
   
  
• Using Constraint Programming to Avoid Overlapping Physical Components in System of Systems  
  by Hamza Cherif Bouchaour, Haffaf Hafid 
  Abstract: The term system of systems (SoS) refers to a collection of independent systems that combine their functionalities to create a higher-level system capable of achieving broader objectives. SoS relies on the integration of multiple independent complex systems into a unified entity. This complexity makes them difficult to design, manage, and maintain. The reconfiguration phase represents a pivotal aspect of SoS, given the interdependence among its constituent systems, which renders them susceptible to disruptive events. When a single system experiences disruption, it can trigger a domino effect that impacts other systems. The distinctive feature of this work is that it has been designed with the specific intention of recovering faulty systems. Previous works have often involved reconfiguration by shifting the system to a degraded mode, discarding faulty components, which can result in the inability to fully achieve the objective. This paper introduces a constraint programming method that enables the system to adapt its parameters to new situations and continue operating. Furthermore, the paper includes a case study that describes a SoS composed of intelligent autonomous vehicles (IAVs) and a gantry crane, which was studied in scenarios where a degraded mode of operation was addressed.
  Keywords: system of systems; SoS; intelligent autonomous vehicle; IAV; road overlap; constraint programming; component failure; system reconfiguration.
  DOI: 10.1504/IJISE.2024.10066925
   
  
• Enhancing Quality Management in Indian SMEs: A Cost of Quality Approach  
  by Mehul Bipin Patel, Darshak A. Desai 
  Abstract: This study will apply COQ principles to small and medium-sized enterprises (SMEs) in India. The study used data collection through surveys and categorised COQ into four main sections. Surveys of employees and management helped determine performance parameters and determine initial COQ levels. The analysis shows significant variations in internal and external failure costs, as well as prevention, appraisal, and assessment costs, between FY 20192020 and FY 20202021, pointing to a varied trend in costs and efficiency. The defect-free production index has slightly improved, indicating continued efforts to maintain quality control. Numerous techniques of industrial engineering and quality engineering were suggested, including 5S, layout modification, Pareto charts, cause and effect diagrams, and check sheets. Utilising a range of quality control instruments and continuous improvement approaches demonstrates the companys commitment to raising operational effectiveness, cutting expenses, and upholding or raising standards for product quality over time.
  Keywords: cost of quality; Indian small and medium sized enterprises; quality management; Pareto analysis; 5S implementation; continuous improvement.
  DOI: 10.1504/IJISE.2024.10066933
   
  
• Empirical Evaluation of Clustering-Based Privacy Preserved Big Data  
  by Saba Anjum Jahangir Patel, Akkalakshmi Muddana 
  Abstract: The term "privacy-preserving data publishing" (PPDP) refers to a concept that offers a number of tools and methods for protecting data privacy while the data is published over the Internet. The significant strategies utilised in the field of privacy-preserving data mining or data publishing are data anonymisation, data randomisation, and cryptography. The major purpose of this survey is to determine clustering-based privacy preserved big data. Based on the literature review classification, present methods are categorised into cluster-based methods, anonymisation-based methods, security-based methods, algorithm-based methods. This survey is established by considering used dataset, toolsets used, published year, performance metrics, classification of methods etc. The research gaps and issues part of the current review papers includes a comprehensive description of the shortcomings. Therefore, the part on research needs is considered as inspiration for continued development of big data with privacy protection.
  Keywords: Clustering; big data; privacy preservation; security; big data mining.
  DOI: 10.1504/IJISE.2024.10067013
   
  
• Ergonomic Intervention for Enhancing Productivity and Reducing Musculoskeletal Disorders in Water Wall Panel Forming Operations: a Case Study  
  by Dinagaran D, Balasubramanian K. R, S.P. Sivapirakasam, Prasanna N 
  Abstract: This study delves into a practical project aimed at fortifying preventive measures against MSDs to ensure the safety of workers. Focused on a water wall (WW) panel forming machine, the project addresses the need for machine setup changes when adjusting the pitch of panel requirements. The current changeover method is associated with weld defects such as fin twist, spatter, weld off line, and undercut. This paper meticulously investigates the root causes of these weld defects and explores the factors contributing to worker fatigue. Employing statistical quality tools, a comprehensive solution is formulated. The developed method not only successfully diminishes worker fatigue but also achieves a remarkable 60% reduction in cycle time, resulting in substantial cost savings of INR 840,000. The initiative significantly mitigates weld defects, enhancing overall operational efficiency. The findings and recommendations have practical applications for firms seeking to enhance safety measures and operational efficiency.
  Keywords: ergonomics; musculoskeletal disorders; MSDs; water wall panel welding; occupational safety.
  DOI: 10.1504/IJISE.2024.10067160
   
  
• Navigating Uncertainty: Intermittent Demand Forecasting in the Furniture Manufacturing Industry through Clustering and Predictive - Case Study  
  by Juan Camilo Gutierrez, Sonia Isabel Polo Triana 
  Abstract: This study addresses the challenge of forecasting intermittent demand in the furniture manufacturing industry, highlighting the combination of clustering analysis and predictive models based on neural networks. Distinctive demand patterns were identified through detailed sales data collection and preparation, followed by advanced clustering techniques. The research delves into using classification and regression LSTM neural networks to predict future demand accurately. Findings reveal that this integrated approach significantly enhances forecasting accuracy, providing a solid foundation for the sector’s inventory management optimisation and production planning. This work underscores the importance of tailoring forecasting strategies to the specific characteristics of intermittent demand, offering valuable insights for the furniture industry.
  Keywords: demand forecasting; intermittent demand; uncertain demand; furniture manufacturing industry; clustering analysis; neural networks; inventory management.
  DOI: 10.1504/IJISE.2024.10067202
   
  
• Effect of Classroom Chair Designs to the Comfort of Students: a Case of a Private Higher Education Institution  
  by Matthew Hendric Chua, Alfonso Gabriel Claros, Cedric Jared Niu, James Florence Santos, Miriam Bongo 
  Abstract: As educational institutions strive to provide proper classroom design that can satisfactorily support the overall academic performance of students, several challenges in performance-influencing factors remain evident. Among such factors, the design of the chairs is the most pressing, considering that the other factors already have a standard protocol to be followed to ensure a conducive environment for students. Along this line, this paper seeks to explore the relationship between chair dimensions, anthropometric measures, posture and comfort. In order to validate the hypotheses formulated based on these relationships, a case study is conducted in a private higher education institution in the Philippines. Key results reveal a significant mismatch between chair dimensions and anthropometric measures and students' posture. Furthermore, students' comfort is also affected by chair dimensions and posture. Such results can provide a guideline to university administrators in improving the classroom design with respect to proper chair dimensions.
  Keywords: anthropometric measures; classroom chairs; comfort; posture; higher education.
  DOI: 10.1504/IJISE.2024.10067205
   
  
• MCDM Analysis for Sustainable Development and Green Growth in Manufacturing industries  
  by Gagandeep Singh Sodhi, Chandan Deep Singh, Navdeep Singh Grewal 
  Abstract: Modern production revolves around five key objectives: quality, cost-effectiveness, timely delivery, continuous improvement, and adaptability. Traditionally, companies achieved these goals through various practices. These include integrating design and manufacturing (often called concurrent engineering) for faster development, implementing quality improvement methodologies like Six Sigma to minimise errors, and streamlining changeovers (setup reduction) to increase responsiveness. However, emerging trends suggest a new path: fostering innovation across high-tech production facilities, with a strong focus on environmental responsibility. This approach acknowledges the critical role factories play in a company's success by directly impacting product quality and the resulting competitive edge. Ultimately, this translates into tangible benefits like increased sales due to superior offerings, a loyal customer base built on satisfaction, a larger market share driven by sales and customer loyalty, improved profitability through efficiency, and opportunities for growth and expansion fuelled by healthy profits. To truly outpace competitors, a company's operations need a strategic advantage derived from prioritised goals. This research focuses on identifying high-impact risk factors within the industry to develop a qualitative model based on their severity and potential consequences.
  Keywords: sustainable development; green growth; MDEMATEL; CRITIC; GRA.
  DOI: 10.1504/IJISE.2024.10067225
   
  
• A Fast Terminal Fractional-order Backstepping Sliding Mode Control for Ball-Balancing Robots  
  by Minh Duc Pham, Cong Minh Pham, Phuong Thao Dao, Thu Giang Do, Nhat Minh Nguyen, Van Trong Dang, Tung Nguyen 
  Abstract: In this paper, we construct a robust controller of the 2D ballbot model with one input and two desired outputs. The combination of back-stepping, fast terminal sliding mode, and fractional calculus becomes Fast Terminal Fractional-order Back-stopping Sliding Mode Control (FTFBSMC) to accomplish both the tracking task and balancing task of the ballbot. Moreover, we have analysed and proved the stability in the finite approach of the controller without zero dynamics of outputs. Numerical simulation results are also used to determine and analyse the efficiency of the control law. Sliding mode control and fast terminal fractional order back-stepping sliding mode control are compared to evaluate the main improvement of the proposed controller in different scenarios.
  Keywords: Sliding mode control; Back-Stepping Technique; Fractional-order; Finite approach; Ball-balancing robot.
  DOI: 10.1504/IJISE.2024.10067272
   
  
• Lean Six Sigma Implementation: Results from the Soft Drinks Industry  
  by Janethkareen K. Kitsovi, Ismail Wilson Taifa 
  Abstract: This study applied the Lean Six Sigma (LSS) methodology in the soft drink industry’s production process to analyse the rejects and propose potential strategies. The rejects produced were assessed using the DMAIC technique. Thirty-eight employees were involved, including 30 production line workers and eight top managers and/or supervisors. Historical soft drink production data showed a drop in a sigma level from 4.7 to 4.5. The deployment of the LSS methodology resulted in the potential saving of Tanzanian Shillings 85 million annually. The study provides strategies which can help factories successfully implement the LSS methodology, including deploying effective management, precise use of LSS tools and techniques, maintenance strategy, the establishment of the production strategic plan, improving reward and recognition, effective employee empowerment, appropriate selection of the project staff, effective cross-function management, linking of customer, business strategy and suppliers to LSS and effective training of staff according to LSS.
  Keywords: Six Sigma; Lean Six Sigma; LSS; LSS methodology; Six Sigma methodology; SSM; DMAIC; product rejects; nine wastes; soft drink industry.
  DOI: 10.1504/IJISE.2024.10067284
   
  
• Chimp Water Wave Optimisation Enabled Random Multimodal Deep Learning for Text summarisation  
  by Rajesh Kumar Cherukuri, Sampath A. K, Manoj L. Bangare, Sanjay Nakharu Prasad Kumar 
  Abstract: Text summarisation is the process of compressing longer documents into a shorter version without losing their overall information contents. However, automatic text summarisation is still a challenging issue due to the unavailability of the corpus. To overcome the issues, a robust summarisation model, named chimp water wave optimisation-based random multimodal deep learning (ChWWO-based RMDL) method is developed for text summarisation. Here, the bidirectional encoder representations from transformers (BERT) tokenisation is accomplished for performing the tokenisation operation. With the tokens, aspect term extraction (ATE) is done to improve the summarisation performance. The RMDL model is employed for the text summarisation process where the developed ChWWO algorithm is utilised for training the RMDL model. However, the devised ChWWO is the integration of the chimp optimisation algorithm (ChOA) and water wave optimisation (WWO). The developed method achieved superior performance with a higher precision of 0.961, recall of 0.971, and F-measure of 0.966, respectively.
  Keywords: text summarisation; chimp optimisation algorithm; ChOA; water wave optimisation; WWO; natural language processing; NLP; random multimodal deep learning; RMDL.
  DOI: 10.1504/IJISE.2024.10067769
   
  
• GSDO: Golden Search Dingo Optimisation Enabled CNN Based Long Short Term Memory with Review Vectorisation for Fake News Detection  
  by Steni Mol T. S, Sreeja P. S 
  Abstract: Fake news detection is a significant chore, which not only guarantees that users deliver authentic information, but also assists in maintaining trustworthy ecosystems. Most of the present detection approaches concentrate on identifying signs from contents of news that are commonly not efficient because fake news is oftentimes purposely written to mislead users by imitating actual news. Presently, the detection of fake news is commonly classified as social context-based learning and news content-based learning. Here CNN_LSTM_GSDO, which is an efficient technique, is designed for fake news detection. Initially, input review data is fed to the review vectorisation phase. WordNet2vec and BERT are the two models utilised in the review vectorisation stage. Finally, fake news detection is performed using CNN-based LSTM, which is a transfer learning approach. It is trained by a newly devised GSDO, which is a combination of GSO and DOX.
  Keywords: long short-term memory; LSTM; WordNet2Vec; golden search optimisation; GSO; Dingo optimiser; DOX; convolutional neural network; CNN.
  DOI: 10.1504/IJISE.2024.10067858
   
  
• An Empirical Study on the Role of Big Data Analytic Performance and its Effect on Retail Business Operations  
  by Amit Mittal, Manjinder Singh, Ruchi Mittal, Varun Malik, Amandeep Kaur, Geetanjali Singla 
  Abstract: The purpose of this study is to examine the influence of big data analytics in the business transformation of the retail industry. The following topics are essential to the study: 1) business transformation of the retail industry; 2) data storage; 3) improving cyber security; 4) perceived usefulness; 5) perceived ease to use; 6) task technology fit. The recommended task is broken into two stages: the first step is to collect the data using the prepared questionnaire from the owners and managers of the retail industry. 219 data were collected using the prepared questionnaire from the owners and managers of the retail industry and the format of the questionnaire is based on the proposed hypothesis. In the second step, the BiGRU deep learning model and SEM analysis are used to evaluate the collected data.
  Keywords: big data analytics; BDAs; retail industry; data storage; cyber security; perceived usefulness; business transformation.
  DOI: 10.1504/IJISE.2024.10068231
   
  
• Bottleneck-Based Decomposition Method for Throughput Estimation of Nonhomogeneous Production Lines with Unreliable Machines and Finite Buffers  
  by Hatice Guner, Alper Murat, Ratna Chinnam 
  Abstract: The paper presents a decomposition method for the throughput evaluation of long serial production lines with non-homogeneous machines separated by finite buffers. The machines have very different processing times and exponential failures and repairs. Even though existing analytical methods provide quite accurate results, they may have challenges in terms of both the computational effort and accuracy when the reliability parameters and processing rates are of different magnitude. To enhance the computational efficiency and reduce the complexity of the decomposition of long lines, we strategically aggregate the production line segments depending on the position of bottleneck machines and represent the long production lines with shorter throughput equivalent ones. Besides, we use generalised exponential distribution for the repair time distributions of the pseudo-machines and develop the decomposition equations. The performance of the proposed method is compared with widely used decomposition methods and simulation using extensive experiments on real-world datasets from the body shops of an automotive OEM. The findings indicate that the proposed algorithm provides highly effective solutions for analysing the throughput of long unbalanced lines.
  Keywords: throughput estimation; approximate model; decomposition; aggregation; equivalent machine; bottleneck.
  DOI: 10.1504/IJISE.2024.10068377
   
  
• Supply Chain Risk Identification and Ranking through fuzzy Delphi and Fuzzy Analytic Hierarchy Process: the Case of the Apparel Industry in Ethiopia  
  by Berihun Bizuneh, Belay Abera 
  Abstract: This paper aims to identify, screen, and rank the supply chain risks, and their sources in the apparel industry in Ethiopia. Data were collected from 20 experts and 392 professionals in 13 companies based on pairwise comparisons and a questionnaire-based survey respectively. The fuzzy Delphi method and fuzzy analytic hierarchy process (AHP) were used to screen and rank the supply chain risks respectively. A descriptive statistic based on the mean score was used to rank the risk sources. The results showed that supply, operational, and infrastructural risks were the top three risk categories. The major sub-supply chain risks included supplier uncertainty, technology, security of resources, safety and security, raw material and product quality, delays in production, and transportation risks. All of the identified sources of the risks were rated at least as significant factors by the respondents. Some mitigation strategies mainly focusing on supply chain collaborations were also proposed.
  Keywords: Supply chain risk; ranking; apparel industry; manufacturing industry; fuzzy Delphi; fuzzy analytic hierarchy process; AHP.
  DOI: 10.1504/IJISE.2024.10068441
   
  
• Chaotic Functions Influenced Spider Monkey Optimisation Algorithm for Optimal Routing and Channel Assignment  
  by Vijay Omprakash Rathi, Raj Thaneeghaivel 
  Abstract: This paper intends to introduce a novel routing and channel assignment in multi-channel MANET. Here, the optimal routing is performed by selecting the cluster head under certain constraints like delay, distance, QoS, RSSI, and security. For this, chaotic functions influenced spider monkey optimisation (CFISMO) algorithm is used. The assignment of channels as the scheduling policy is introduced through senders while it has packets to transmit. In this work, the channel assignment will be initiated via a machine learning model that predicts the availability of the channels, which is based on the paths (channels) generated under the selected cluster head. Here, an optimised neural network (NN) will be used. Thus, the final output shows the paths (channels) to be assigned for data transmission. In the end, the performance of the adopted routing approach is evaluated over other traditional schemes based on various metrics like distance, PDR, delay, energy, alive nodes, QoS, security, and trust, respectively.
  Keywords: MANET; optimal routing; quality of service; neural network; optimisation.
  DOI: 10.1504/IJISE.2024.10068447
   
  
• Self Adaptive Meta Heuristic Model for Floor Planning in Very Large Scale Integration  
  by Bhavya A. B, Vinay Kumar S. B 
  Abstract: The process of designing very large scale integrated (VLSI) circuits includes a stage known as floor planning. Floor planning is done to ascertain the relative location of the various modules inside each sub-circuit after the complex circuit is divided into smaller sub-circuits during the partitioning stage. The objectives of this process are to minimise the total chip area covered by the circuit, the amount of dead space in the layout, and the interlinking wire length (WL) among modules. Estimating the placements and forms of the modules is what it is all about. The integrated circuit (IC), which has small feature sizes, a high clock frequency, and high packing density, can dissipate a lot of heat. This work proposes a self-adaptive cat swarm optimisation (SA-CSO) to resolve the floor plan issues. The non-slicing floorplans are regarded by the algorithm as having hard modules with continuous outline restraint. Thus, the area of layout gets reduced.
  Keywords: very large scale integrated; VLSI; floorplan; non-slicing plan; dead space; area; SA-CSO algorithm.
  DOI: 10.1504/IJISE.2024.10068500
   
  
• Analysis of Barriers to Adopting Blockchain Technology in Green Supply Chain using DANP-Delphi Approach  
  by Mohammad Reza Fathi, Seyed Mohammad Alavizadeh, Abolfazl Khosravi, Mohammad Hosein Soleimani Sarvestani 
  Abstract: The study identifies and ranks barriers to adopting blockchain technology in green supply chains (GSC). It employs survey and applied research methods, utilising field studies and questionnaires for data collection. Initial steps included library research and reviews of scientific literature to compile potential barriers, which informed the questionnaire design. A multicriteria decision-making approach ranked the barriers based on importance, using the Delphi technique and the DANP method. Findings highlight technological barriers as the top priority, with key issues including access to technology, technological immaturity, privacy concerns, and reliance on blockchain operators. Intra-organisational barriers are the second priority, featuring challenges like lack of managerial vision, insufficient blockchain knowledge, and limited financial resources. Inter-organisational barriers, ranked third, include collaboration challenges and stakeholder non-participation. Lastly, extra-organisational barriers, such as laws and regulations, market uncertainties, and non-adoption by industry, are also critical.
  Keywords: Blockchain Technology; Supply Chain; DEMATEL; Delphi.
  DOI: 10.1504/IJISE.2024.10068601
   
  
• Residual Preload Prediction Software System Design for Casing Tail Nozzle Bolting System based on Elastic Interaction Stiffness Theory  
  by Wujiu Pan, Xi Li, Hengyang Xu, Yuanbin Chen, Junyi Wang, Jianwen Bao, Xianjun Zeng 
  Abstract: Bolt connection structure is widely used in aerospace parts connection, due to the influence of elastic interaction, the preload of bolt connection will change, so it is necessary to establish a prediction model for the residual preload of the bolt group. Based on the spring-node model and the elastic interaction theory, this paper designs a software system for predicting the residual preload force of the casing tail nozzle bolting system. The system is developed based on MATLAB, which reads the data entered by the user and solves for single-bolt single-step, single-bolt multi-step, and double-bolt for different preloading methods. Predicts the distribution of residual preload of a bolt group under any initial preload and tightening sequence according to different preload sequences. The software can display initial data, prediction result graphs and prediction data and export them, which greatly improves the efficiency and accuracy of preload prediction.
  Keywords: Elastic interaction;Bolt stiffness;Bolt pre-tightening;Casing tail nozzle; Software system development.
  DOI: 10.1504/IJISE.2024.10068629
   
  
• Optimised NN-Based Flood Prediction on River Morphological Changes in the Ganga River  
  by Chandan Raj, Vivekanand Singh 
  Abstract: Floods have become a geo-environmental hazard, which has turned out to be a disaster as it has a destructive effect on the economy and society. Nowadays, several flood prediction systems have evolved that consider the previous flood events, from which the upcoming flood events can be predicted. This work develops a new flood prediction model on river morphological changes, particularly in the Ganga River. The suggested design is divided into two main parts: 1) feature extraction; 2) classification. At first, the input is given for the feature extraction phase, where the vegetation index features and water index features are extracted. After this, the extracted vegetation and water index features are classified, where an optimised deep neural network (DNN) is deployed. Furthermore, the DNN weights are adjusted using the self-improved lion algorithm (SI-LA) to increase the created approachs accuracy.
  Keywords: Flood prediction; River morphology; Vegetation index; Deep Neural Networks; Self Improved Lion Algorithm.
  DOI: 10.1504/IJISE.2024.10068743
   
  
• Streamlining Internal Logistics in the Automotive Industry: a Simulation-Based Optimisation Framework within Lean Production  
  by Yongzhong Wu, Qianyu Zhang, Yuxiang Wu 
  Abstract: Lean production principles have been extensively adopted in the automotive industry, primarily targeting the minimisation of inventory levels and cycle times. However, as the industry confronts fierce competition and evolving demands, internal logistics costs within factories have emerged as a significant portion of total operational expenses. The complexity of internal logistics is further compounded by the involvement of multiple resources and intricate processes. This paper introduces a simulation-based optimisation approach designed to streamline internal logistics, focusing on minimising resource utilisation across various processes while adhering to constraints on work-in-progress (WIP) levels and cycle times. The proposed methodology was successfully implemented in a component supplier within the automotive sector, yielding an 814% improvement in resource utilisation and a 12% reduction in annual logistics costs. These findings underscore the potential of simulation optimisation to enhance operational efficiency and lower internal logistics costs in complex lean production environments.
  Keywords: internal logistics; discrete event system; simulation; simulation optimisation.
  DOI: 10.1504/IJISE.2024.10068794
   
  
• Evaluating the Impact of Prognosis Agent Technology in Manufacturing: a Hybrid AHP and DEMATEL Approach  
  by Vikram Singh, Somesh Kumar Sharma 
  Abstract: This paper analyses the effects of Prognosis Agent Technology (PAT) on production and control in manufacturing organisations. While various agents have been developed for design, manufacturing, and defect identification, limited research exists on the impact of PAT in manufacturing using the Analytical Hierarchy Process (AHP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) approaches. The study constructs a hierarchical framework for PAT, drawing data from a literature review. Using the AHP approach, the paper designs a performance rating system to assess the weights of PAT factors and sub-factors based on expert input. The DEMATEL approach is then applied to examine the relationships and direct, and indirect effects of these factors on manufacturing performance. The findings from both methodologies demonstrate that implementing PAT significantly enhances productivity and control in manufacturing organisations, underlining its potential to improve operational efficiency and decision-making in the sector.
  Keywords: PAT; AHP; DEMATEL; Productivity & control of manufacturing organization.
  DOI: 10.1504/IJISE.2024.10069125
   
  
• An Efficient Algorithm for the Dynamic Job Shop Scheduling Problem with Order Rejection, Inventory Management, and Machine Breakdowns  
  by Mostafa Jafari, Amir Hossein Akbari 
  Abstract: This paper investigates a dynamic job shop scheduling problem inspired by real-world manufacturing challenges, incorporating parallel machines, order rejection, tardiness costs, machine deterioration and inventory costs. Due to capacity constraints, some orders must be rejected to ensure timely delivery. Machines degrade over time, increasing processing times, and require maintenance when deterioration exceeds a threshold. Unexpected breakdowns also occur, requiring either costly urgent repairs or more economical but time-consuming repairs. A mathematical model is developed with the objectives of maximising profit, minimising the variance in inventory costs across different periods, and minimising the variance of machine workload. To solve the problem, a heuristic algorithm combining genetic algorithms and artificial neural networks is proposed. The model is validated using real-world data, and the results confirm the effectiveness of the proposed solution. This research provides valuable insights into optimising scheduling, maintenance, and inventory decisions in dynamic manufacturing environments.
  Keywords: Dynamic Job Shop Scheduling Problem Order Rejection Inventory Management Artificial Neural Network Machine Breakdown.
  DOI: 10.1504/IJISE.2025.10069284
   
  
• Multi-Objective Optimisation of the Heterogeneous Capacitated Vehicle Routing Problem for Rice Commodities Considering Shrinkage and Carbon Emissions  
  by Tari Hardiani Safitri, Achmad Pratama Rifai, Wangi Sari, Erny Rahayu Wijayanti, Luluk Lusiantoro 
  Abstract: This research implements the Multi-Objective Heterogeneous Capacitated Vehicle Routing Problem (MO-HCVRP) for rice distribution, considering both weather conditions and carbon emissions. The study aims to achieve two conflicting objectives: minimizing transportation costs, which include vehicle rental fees and carbon emission taxes, and shrinkage costs due to rice damage because of weather conditions. Multi-Objective Adaptive Large Neighborhood Search (MOALNS) is developed to solve the problem. Several novels destroy and repair operators of MOALNS are proposed. The performance of MOALNS is compared with that of Non-dominated Sorting Genetic Algorithm II (NSGA-II) to evaluate the proposed method's efficacy. Both algorithms are tested on empirical datasets from the real-world rice distribution problem in Indonesia. The comparison shows that MOALNS can generate a superior Pareto Front in terms of convergence and proximity to the True Pareto Front. In contrast, NSGA-II demonstrates a better Pareto Front in terms of spread or diversity within the search space.
  Keywords: Heterogeneous Capacitated Vehicle Routing Problem; Multi-Objective; Rice Distribution; Shrinkage Cost; Carbon Emission; Multi-Objective Adaptive Large Neighborhood Search (MOALNS).
  DOI: 10.1504/IJISE.2025.10069395
   
  
• Advanced Fault Diagnosis and Measurement System Capability Analysis: Enhancing Accuracy and Reliability in Industrial Applications  
  by Ashokkumar N 
  Abstract: Machine learning (ML)-based classification systems are needed to keep estimates up to date and figure out what is wrong. But these systems can fail, and when they do, it can be very dangerous, like when fake alarms give you more time to wait. You can find confidence bands in this model that tell you if an input can fit into a box or not. Everything that was put in makes up the bands. It is better to say that the outcome was not in the right group than that it was in the wrong group. A model of an artificial neural network (ANN) that was taught on data about shaking can help it find issues with energy use. With this method, you can use any motor speed. A quick and easy way to figure out what is wrong with fans is to watch how they shake. You can save money on care and get a better handle on the process if you do not have to shut down as often. It works better when the machine knows more about what goes wrong with data. People can now choose better things.
  Keywords: Bearing Detection; Detection Transformer; Artificial Neural Network Model; Predictive maintenance and smart methods; SH-FT approach.
  DOI: 10.1504/IJISE.2025.10069430
   
  
• Optimizing Sustainable Absorbent Selection to Advance Greener Solutions in the Refrigeration Industry  
  by Ashwin Pathak, R. S. N. Sahai 
  Abstract: In the contemporary era, the refrigeration and air conditioning sector is focusing on the sustainable solutions for addressing the environmental challenges In the present study, the absorbents for the refrigeration systems have been analysed and best alternative among the 4 shortlisted ones has been selected The criteria used for the evaluation were sustainability, economical aspects, safety and environmental risk, technical performance, and thermodynamic properties with a total of 20 sub-criteria. The powerful integrated decision support system was developed for addressing the case problem in the multi-criteria environment. This is the pioneering research in the refrigeration sector in which the absorbents are evaluated and ranked focusing on the environmental performance. After carrying out the analysis, a subfactor, namely energy efficiency and the main criteria sustainability were found to be most crucial. Also, out of four of the shortlisted ones, Absorber 3 was found to the best alternative. The social, academic, practical implications of the study have been offered.
  Keywords: Absorber selection; Environmental impact analysis; Decision support system; Sustainability assessment; Sustainable refrigeration systems.
  DOI: 10.1504/IJISE.2025.10069431
   
  
• A Systematic Review of the Safety of Workers in the Construction Industry using IoT  
  by Leena Rakesh Jadhav, Manoj Hudnurkar 
  Abstract: Among all the other trades the construction environment is situated as the foremost contributor of deaths. Based on the present Statistics, there are lakhs of construction injuries that happen each year, and a major amount of injuries are caused by falls or slips from heights. This survey paper conducts the assessment of different approaches developed for the safety of workers in the construction industry using IoT. Here, the presented schemes from the collected articles are classified into IoT-based, ML-based, DL-based, Optimisation-based, Fuzzy-based, and RFID-based models. The review is analysed through publication year, implemented design, and performance metrics. Furthermore, the obtainable limitations in articles are listed evidently in the research gaps section. Therefore, based on the research gaps, it is reflected as an inspiration for the expansion of valuable security approaches to assist workers in construction sites.
  Keywords: Construction industry; safety of workers; Internet of Things (IoT); Risk management; Deep Learning (DL).
  DOI: 10.1504/IJISE.2024.10069661
   
  
• Simulation Modelling and Analysis for System Design and Control Systems using Java: a Comprehensive Approach  
  by Lin Zhou, Xiaoli Jia 
  Abstract: The projects goal is to create a fresh, cost-free Java tool that can assist in creating embedded control systems. These tools are based on a set of TrueTime tools for MATLAB. They were made to do this. Writing code in Java can provide a sense of control over real-time processes. You can learn how to create tools that operate in real-time and perform multiple tasks simultaneously. They already know how to use our tools because they know how to write Java code. Plenty of people like to plan and write with EJS, and more and more schools are beginning to use it too. The library works well with EJS, which is another reason. Even people who are not very good at programming can use EJS to make Java models that are both easy and hard. Why did they build that new library? How did you do it? How could this piece be used? It is made for people who write EJS and Java code. At http://lab.dia.uned.es/jtt, you can see the JTT library and hear some live sounds.
  Keywords: TMSLAB Virtual Simulator; Sample Implementation; Background; Synchronous Dataflow (SDF) MoC; Imperative Programming Paradigm.
  DOI: 10.1504/IJISE.2025.10069992
   
  
• Sustainable Operations Management Model in a Non-Profit Organisation  
  by Martha F. P. Harmse, Wilna L. Bean 
  Abstract: The sustainability of non-profit organisations (NPOs) is at risk. They can improve their sustainability through operations management, but require appropriate sustainable operations management models (SOMMs) to address the complexities that surround their operations. This paper reports on an action design research study to develop a SOMM in a specific NPO. Simultaneously, a theory is contributed of how appropriate SOMMs can be developed in other NPOs. The proposed SOMM addresses paradox to foster and reinforce commitment to multiple, competing strategies so that the NPO became more fluid, enhanced their reflexive self-regulation, and became more sustainable. The paper furthermore proposes how various challenges can be addressed when developing a SOMM in an NPO. These include definitions of sustainability and of NPOs, an integrated organisational perspective of a SOMM, and sustainable operations management in NPOs as an organised complex problem.
  Keywords: sustainability; sustainable operations management; operations management model; NPO; action design research; reflexive self-regulation; integrated organisational perspective; organised complex problem.
  DOI: 10.1504/IJISE.2025.10070001
   
  
• Ranking of Factors affecting Performance of Indian Manufacturing Industries using Fuzzy WSM Technique  
  by Rajdeep Singh  
  Abstract: Core functional competences are important in businesses to improve market competitiveness. These enhance a company's capabilities, allowing it to function more rapidly, with greater flexibility, or with a higher level of dependability than competitors. Various methods and approaches, such as quantitative and qualitative analysis tools, are utilized to analyse these factors. The purpose of this article is to investigate and analyse the core functional competencies utilising the fuzzy WSM tool in multi criteria decision making procedures. Various criteria were found and prioritised during the research utilising the fuzzy WSM (weighted sum model), which can benefit the company by providing a better understanding of core functional skills.
  Keywords: Competitiveness; fuzzy WSM; MCDM; technical competency and core functional competencies.
  DOI: 10.1504/IJISE.2025.10070002
   
  
• Application of DMAIC Using Poka Yoke In Preventing Packing Products Without Sufficient Amount of Accessories - a Case Study In Electric Hand Tool Manufacturing Factory  
  by Le Duy Khanh, Do Ngoc Hien, Le Dao 
  Abstract: Manufacturing companies are highly concerned about product packages that do not have enough accessories. This mistake may cause dissatisfaction to the customer, and more seriously, this can lead to the manufacturer's reputation decrease on the market, and orders may be canceled. Consequently, this study aims to use the DMAIC Cycle (DMAIC) to solve this problem for an Electric Hand Tool Product Manufacturing Company. It shows how to implement each cycle step from Define to Control to improve processes and equipment to reach the target. Useful tools like Pareto Chart, Cause and Effect Diagrams and so on are applied to analyze and indicate the root cause and improvement milestones. The failure rate reduced significantly after the Improve stage, which applies mistake-proofing (Poka-Yoke) to prevent mistakes from happening and bad products from escaping from the production line and coming to the customers. The study also lists out the activities that will have to follow to maintain the project’s effectiveness.
  Keywords: DMAIC Cycle; Process and Equipment Improvement; Voice of Customer; Mistake Proofing Poka Yoke; Manufacturing; Lean Six Sigma.
  DOI: 10.1504/IJISE.2025.10070110
   
  
• Calculating the Reliability of the Outsourcing Process in the Supply Chain  
  by Zahra Zare, Maysam Ashrafzadeh 
  Abstract: Outsourcing involves utilizing external resources and suppliers to perform certain activities of an organization for various reasons. In outsourcing decisions, alongside strategic issues, considerations such as cost-benefit analysis, financial evaluation, and risks related to supplier quality, the reliability of lead times, and delivery are also considered. When all these factors are considered together, outsourcing decisions can become highly complex, impacting profitability, investment, working capital, borrowing, and competitive positioning. This article examines and calculates the reliability of the outsourcing process for service providers in the supply chain in fulfilling their duties, which include delivering a product or service at a specified time with a specified quantity and quality. Methods for increasing the reliability of the outsourcing process for service providers are also discussed. In addition, an approach is proposed to help managers make decisions about the reliability of the outsourcing process for service providers and to consider risks and challenges.
  Keywords: Outsourcing - Reliability - Supply Chain - Outsourcing Risk Management -Outsourcing of Service Providers.
  DOI: 10.1504/IJISE.2025.10070169
   
  
• Data-Driven Model Forecasts of Exchange Rates  
  by Dongwei Shi 
  Abstract: This paper introduces an innovative methodology for analyzing the periodic influence factors on exchange rates. The majority of current methods, which focus on examining both macro and micro influence factors as well as the risk factors associated with currencies, typically rely on regression models. Our proposed algorithm employs a variable selection technique that integrates the orthogonal maximal information coefficient, feature interaction, and random forest (OMICFI-RFVS), taking into account the correlation, redundancy, and interaction among variables in a comprehensive manner. The empirical findings indicate that the primary factors influencing exchange rates vary across different time frames. Specifically, in the long term, exchange rates are more likely to be influenced by the exchange rate regime, while in the short term, they are more susceptible to economic indicators and the exchange rates of other significant currencies.
  Keywords: exchange rate; periodical influence factors; OMICFI-RFVS algorithm.
  DOI: 10.1504/IJISE.2025.10070288
   
  
• Investigating the Impact of Gamification Elements on a Production Environment  
  by Cristoff Mavrick Bata, Jimwel Lao, Elizabeth Jill Ong, Darrence Matthew Tanhuy, Darlyn Jasmin Magno Rodriguez 
  Abstract: In modern industrial settings, developing workers' skills is crucial for enhancing efficiency. Gamification has emerged as a strategy to improve performance in manufacturing and logistics, yet there is limited research on how task complexity and reward types affect its effectiveness. This study examines gamification's impact on productivity and error rates in a garment production facility. Collaborating with industry experts, game designers, and managers, game mechanics were aligned with company goals and worker motivations. A six-day experiment with 27 workers across four processes found that tangible rewards, like monetary incentives, were more effective than intangible ones in improving performance. Moreover, moderate goal difficulty was found to optimize productivity and reduce errors. This research highlights the importance of tailored gamification approaches that balance task complexity and motivation, providing valuable insights for improving worker performance in industrial settings.
  Keywords: gamification; Octalysis; production; intrinsic motivation; task complexity; reward types.
  DOI: 10.1504/IJISE.2025.10070345
   
  
• Data-Driven Project Management through Big Data Analytics and Machine Learning: Technology Adoption and Policy Recommendations in the Philippines  
  by Darlyn Jasmin Magno Rodriguez 
  Abstract: Big data analytics (BDA) and machine learning are transforming industries, including project management. The Philippines aims to become a "big data processing hub" and must address trends, challenges, and policy recommendations related to these technologies. Key areas include real-time data analysis, data protection, and integrating machine learning into project management for better cost estimates, resource optimisation, risk control, and outcomes. The Philippines benefits from improved infrastructure, government support, and collaborations like Project ALaM but faces challenges like data privacy, high broadband costs, and the need for upskilling investments. Policy recommendations include comprehensive training, prioritising STEM education, enacting digital transformation laws, and enhancing ICT infrastructure. Overcoming these challenges is essential for the Philippines to unlock the full potential of big data and machine learning for successful implementation.
  Keywords: Big Data Analytics; Machine Learning; project management policies; technology adoption; Philippines.
  DOI: 10.1504/IJISE.2025.10070474
   
  
• A Big data Perspective for Intellectual Stock Market Prediction Approach  
  by Shilpa B. L, Shambhavi B. R 
  Abstract: This work aims to develop a new stock prediction model, where, two sets of input namely, sentimental data and stock data are considered. The data gathering is done from a big data perspective. Initially, the sentimental data is pre-processed using modified stemming and tokenization. From the stemming output, features like BOW, proposed TF-IDF and N-gram features are derived. Further, the tokenised outputs and stock data are processed with the Hadoop framework. Then, the technical indicators such as (ATR, Bollinger bands, ADI and EOM are derived. Further, feature selection is carried out by improved chi-square. Finally, the forecasting takes place by optimised RNN, where, the weights are optimized via Chaos-based Self Updated Beetle Swarm Optimisation (CSU-BSO). Further, analysis is done based on varied error metrics.
  Keywords: Stock market; Sentimental data; Hadoop framework; Optimized RNN; CSU-BSO Model.
  DOI: 10.1504/IJISE.2025.10070484
   
  
• The Engagement of Cultural Society, Waste Information System, Task Process and Green Environment Marketing within The Waste Management Supply Chain in the Urban Context of Semarang  
  by Ratih Setyaningrum, Arif Nugroho, Ravindran Balasubramani 
  Abstract: Semarang City, the capital of Central Java Province, faces problems in waste management due to the increasing volume of waste disposal from the community It is necessary to carry out waste management based on the supply chain regulated from upstream to downstream to reduce the amount of waste and utilize waste that is still useful A good waste management supply chain is usually based on variables such as culture society, tools and technology (Waste Information System), task processes, and marketing green environment An analysis was carried out using the Structural Equation Modelling method to determine the variables that have the most significant influence on the waste management supply chain This research was conducted in Semarang City by taking data from 224 residents of Semarang City as respondents From the results carried out, it was concluded that the variables that most affect the supply chain of waste management in Semarang City are tools and technology with a value of 60%; the second influential variable is the task process variable, with a value of 30%, then the variable that is also influential is the marketing green environment variable with a value of 18%.
  Keywords: Waste Management; Supply Chain; Culture Society; Green Environment Marketing; Structural Equation Model.
  
  
• Development of a Smart Cyber-Physical System for Adaptive Manufacturing: Integrating Mechanical Design Optimisation, Electrical Automation and Industry 4.0 Technologies  
  by Jiafang Han 
  Abstract: A Cyber-Physical System (CPS) constitutes an integrated framework in which physical and computational components interact seamlessly. In the context of modern business and manufacturing, the growing demand for substantial transformations has rendered business operations increasingly complex and dynamic. As a result, a significant innovation in this domain is the Smart Cyber-Physical Manufacturing System (Smart-CPMS). Proponents of Industry 4.0 regard Smart-CPMS as a fundamental advancement, offering substantial advantages over traditional manufacturing methodologies. The core principles of Smart-CPMS are derived from biological systems, enabling its components to be customized according to specific business requirements. A comprehensive understanding of this concept necessitates an in-depth examination of biological systems, as they serve as the foundational basis. The CPS framework is supported by various technological components, including Radio-Frequency Identification (RFID) and sensor networks. Furthermore, the Internet of Things (IoT) plays a crucial role in facilitating real-time data exchange and enhancing operational efficiency.
  Keywords: Smart Cyber-Physical System; Industry 4.0 Technologies; Electrical Automation; Internet of Things (IoT); Radio Frequency Identification (RFID) systems; human-machine interfaces (HMIs).
  
  
• Classification of Power Quality Failures by Deep Ensemble Model  
  by Vishakha Saurabh Shah, Ali M. S., Saurabh A. Shah 
  Abstract: This developed method includes three major sections (i) Data Normalization (ii) Feature Extraction and (ii) Classification. At the initial phase, the collected PQ data is normalized in the data normalisation step. Then, the normalised data is given as input to the feature extraction. The time series features like Rate of Change (ROC), Relative Strength Index (RSI), Simple Moving Average (SMA) and Standard Deviation features were extracted. Then, the extracted features are trained by the ensemble classifier consisting of Convolutional Neural Network (CNN) and Cascaded Neural Network (Cas NN). The classifiers CNN and Cas NN run independently and concurrently with extracted features. The final output is taken by averaging the output of both classifiers. Finally, a comparative evaluation is undergone to validate the performance of the projected model with traditional classifiers and feature analysis.
  Keywords: Smart meters; Utilities; Power quality; Data normalization; Renewable energy.
  
  
• An Artificial Neural Network Based Prediction of Critical Nugget Diameter in Resistance Spot Welding of Automotive Steels  
  by M.D. Tahsin Akhtar, Mahesh Kavre 
  Abstract: This study presents a data-driven approach for predicting the critical nugget diameter (?C) in resistance spot welding, a key parameter influencing weld integrity. ?C represents the transition point from brittle interfacial failure (IF) to the more desirable pull-out failure (PF), which enhances energy absorption capacity. Predicting ?C without conducting physical welding experiments is crucial for approving new steel grades efficiently. To address this, an artificial neural network (ANN)-based model was developed using spot welding data from 62 published studies. The model predicts ?C based on steel properties, including thickness, chemical composition (C, Mn, Si), mechanical properties (yield stress, ultimate tensile strength, elongation), and coating type. The ANN model was validated against experimental data, demonstrating its reliability and potential for generalized application. This research contributes to advancing predictive modeling in welding, reducing experimental costs, and streamlining steel grade approvals in industrial applications.
  Keywords: Resistance spot welding (RSW); Critical nugget diameter; Artificial Neural Network (ANN); Automotive Steels; Prediction model.
  
  
• Construction of Prediction Model for Individual Investors’ Psychology and Behavior Based on Cognitive Neuroscience  
  by Guangdong Liu, Sang Fu, Shiyong Liu 
  Abstract: Traditional forecasting models cannot extract the trend information of retail investors' multi-scale psychological and behavioural data, and the predictions are not accurate. To solve this problem, a Markov-based individual investor psychology and behaviour prediction model is proposed. Using the wavelet multi-scale analysis method , the multi-scale data of individual investor's psychology and behaviour are extracted. A long-term-memory analysis is performed on multi-scale data of individual investors’ psychology and behaviour using the correlation analysis method, and the trend information is extracted. On this basis, a Markov prediction model is established, and a modified investment preference model is introduced to improve the accuracy of the prediction. Using the individual similarity degree, the nearest neighbour set of the target individual is established, and a multi-order predictive Markov fusion model for multiple individuals is formed to achieve accurate prediction. The experimental results show that the proposed model achieves better nonlinear fitting and higher prediction accuracy.
  Keywords: individual investors; psychology and behaviour; prediction model; Markov.
  DOI: 10.1504/IJISE.2022.10046762
   
  
• Reliability assessment of a NSP system under constant triangular fuzzy failure rates  
  by S. Malik, S.C. Malik, N. Nandal, A.D. Yadav 
  Abstract: Here, the reliability of a non-series-parallel system (NSP) has been examined considering fuzzy failure rates. There are seven non-identical components in the system, which are arranged into three structures. The two structures operate in parallel and each having three components connected in series; while the third structure has a single component connected with the extreme components of the parallel structures. The expression for reliability of the system is assessed using the path tracing method. The failure rate of the components is assumed as constant triangular fuzzy number and thus they follow the exponential distribution. The α-cut method is used to defuzzify these fuzzy numbers for determining reliability measures. The intervals for fuzzy reliability and MTSF of the system have been computed for both non-identical and identical components. An illustration of RLC system has been described to highlight the application part of the research work.
  Keywords: fuzzy reliability measures; NSP system; exponential failure laws; α-cut approach; triangular membership function.
  DOI: 10.1504/IJISE.2023.10060854
   
  
• Optimising parcel count in e-commerce fulfilment with mixed-split order picking  
  by Wen Zhu, Jingran Zhang, Sanchoy Das 
  Abstract: E-commerce fulfilment warehouses (e-warehouses) store thousands of items and fulfil thousands of online customer orders every day. E-warehouses are operationally different from traditional warehouses. To accelerate fulfilment speed, an e-warehouse splits multi-line orders across multiple picklists. A key research question is how to manage the flow of picked items so that the number of shipped parcels is minimised. This research introduces the e-warehouse order consolidation (WOC) problem. Tote consolidation is a key link between order picking and parcel packing. We identify key modelling elements and formulate the associated constraints and objectives. The WOC mixed integer program is tested on a series of problems, and we illustrate the operational and business value of controlling the tote consolidation process. Order similarity between totes is used to develop two fast heuristics. Two controllable design parameters are investigated, the number of packing stations and the number of totes assigned simultaneously, on parcel packing efficiency.
  Keywords: fulfilment speed; shipping costs; parcel packing.
  DOI: 10.1504/IJISE.2023.10061212
   
  
• An enhanced fractional-order fuzzy controller design for an integrated power system using a counteractive control action  
  by Devbrat Gupta, Jitendra Kumar, Vishal Goyal 
  Abstract: This research article reports an efficient control of the integrated power system (IPS) using a fractional-order fuzzy proportional and derivative (FOFPD) controller combined with a fractional-order integral and derivative (FOID) controller in order to overcome the sudden variation in microgrid frequency problem. The novelty of the anticipated control strategy lies in the use of FOID control action, which generates the counteractive action to improve the control performance. The controller’s gains are optimised by an optimisation algorithm called spider-monkey optimisation (SMO). The objective function is considered as the sum of the integral of the squared deviation of the microgrid frequency (ISFD). The proposed controller’s response is then compared with the integer-order counterparts to investigate the effectiveness of the suggested controller. The detailed simulation results demonstrate the robust behaviour of the proposed control scheme and establish its superiority over other investigated control structures.
  Keywords: integrated power system; IPS; fractional-order; fuzzy PID controller; spider-monkey algorithm; micro-grid frequency.
  DOI: 10.1504/IJISE.2023.10061475
   
  
• Impact of multi-agent technology on the manufacturing organisations: a multi-criteria decision-making analysis  
  by Vikram Singh, Somesh Kumar Sharma, Prakhar Shukla 
  Abstract: Quality is a major concern for manufacturers and can affect the performance of manufacturing system components and product quality. This study aims to improve the quality of manufacturing processes from material acquisition to the end of production using multi-agent technology (MAT). The literature review identified five factors and their 31 governing variables, and their impact is analysed through AHP, DEMATEL, and TOPSIS. AHP was used to study and establish priority orders. DEMATEL was used to develop inter-relationship and TOPSIS to validate the global ranking evolved through AHP. ‘Manufacturing process’ along with ‘quality aspects’ have evolved most significant factors for controlling quality. Their significance is increased since they were discovered to be the most influential in affecting other factors. The detailed research and discussions in this article may allow industrial organisations to raise quality standards, hence increasing customer support, lowering costs, and improving efficiency.
  Keywords: analytic hierarchy process; AHP; DEMATEL; manufacturing organisational; multi-agent technology; MAT; TOPSIS.
  DOI: 10.1504/IJISE.2023.10062063
   
  
• Optimising multiple sclerosis detection: harnessing cutting-edge MRI image analysis for advanced industrial diagnosis  
  by Mohammed Said Obeidat, Hussam A. Alshraideh, Abedallah A. Al Kader, Rabah M. Al Abdi, Morad Etier, Mohammad Hamasha 
  Abstract: Human brain disorders are those abnormal changes that occur around or inside brain parts. These disorders include infections, tumours, trauma, degeneration, structural defects, stroke, and autoimmune disorders. The devastating consequences of brain disorders on the lives of humans could be reduced by early diagnosis. The diagnosis of brain disorders consumes higher time and effort by physicians compared to computerised diagnosis techniques. Several computerised diagnosis algorithms have been developed to improve and optimise the diagnostic capabilities of physicians. Magnetic resonance imaging (MRI) is an effective tool used for brain disorders diagnosis. MRI detection of multiple sclerosis (MS) is extremely complicated due to several reasons, including the anatomical variability between patients, lesion location, and the variability in lesion’s shape. This paper reviews several computerised algorithms used in diagnosing brain disorders, to present the most efficient techniques that reduce the physicians’ diagnosis time and effort of MRI images, hence, starting MS treatment at earlier stages.
  Keywords: magnetic resonance imaging; MRI; brain disorders; industrial engineering algorithms; decision; multiple sclerosis.
  DOI: 10.1504/IJISE.2023.10061152
   
  
• Development of distributed LSTM framework to forecast transportation lead time  
  by Utkarsh Mittal, Dilbagh Panchal 
  Abstract: This study aimed to develop an AI-based system to evaluate delivery complexities and reduce system vulnerabilities more accurately. The approach of the study is empirical where dataset from different systems is used to develop ML and DL models to forecast more accurately transportation time and improve profitability. Various models, e.g., linear regression, deep learning, and distributed long short-term memory (DLSTM) networks are used. It is found that the DLSTM regression model shows superior performance in forecasting the delivery times compared to the other models, achieving an accuracy of around 90%, as the model has the ability to handle complex and nonlinear relationships among variables. The findings underscore the potential of machine learning (ML) and deep learning (DL) in improving predictability and profitability aimed increasing digitalisation in global transportation.
  Keywords: machine learning; deep learning; delivery time forecasting; profitability optimisation; fuzzy C means clustering; supply chain risk management.
  DOI: 10.1504/IJISE.2023.10061303