International Journal of Industrial and Systems Engineering (94 papers in press)

Regular Issues

• Stability Analysis of Asynchronous Switched Positive Systems with Unstable Subsystems  
  by Jingjing Hu, Pingping Gu, Huiwen Liu, Dexiang Liu 
  Abstract: This paper investigates the stability problems of asynchronous switched positive systems based on mode-dependent average dwell time method in continuous-time context. While using mode-dependent average dwell time to study the stability of switched systems, each subsystem must be stable, otherwise a state feedback controller should be designed for the unstable subsystem. But in practical application, when the subsystem is activated one by one, it usually takes a period of time to identify which one of the state feedback controllers should be activated, which causes the asynchronisation. Next, in consideration of the difficulty of designing an appropriate state feedback controller for some unstable subsystems, this paper is aimed at obtaining the stability condition of asynchronous switched positive systems with both stable and unstable subsystems.
  Keywords: asynchronization; mode-dependent average dwell time; stabilization; switched positive systems; unstable subsystem.
  DOI: 10.1504/IJISE.2020.10016058
   
  
• Insightful Implementation of Lean Tools to Cultivate Lean Culture in Small Scale Manufacturing Organization a case study  
  by Jaydeepsinh Ravalji, Shruti Raval, Gulamkhwaza Qureshi, Himadri Shukla 
  Abstract: Small and medium-scale organisations are the backbone of the Indian manufacturing sector. Awareness and proper use of the Lean approach can improve their productivity. This paper demonstrates insightful use of some of the Lean tools to a small subcontractor organisation; for improvement in its current process with less expenditure. The second objective was to develop an attitude among production people for waste-free practices through innovative ideas. To achieve these objectives, the current state VSM was prepared to identify wastes in the process. Kaizen, Pacemaker, PEEP, and two-Bin Kanban system were used to achieve the ideal process. By implementing these tools, the total cycle time for one rotor assembly is reduced by 37.12%, the total lead time is reduced by 7.1%, and inter-departmental material movement per day is reduced by 37.5%. This paper will motivate researchers and practitioners to develop specific but effective solutions with knowledge of Lean philosophy.
  Keywords: lean manufacturing; 5S; value stream mapping; VSM; Kaizen.
  DOI: 10.1504/IJISE.2021.10041486
   
  
• Cost Optimality of an erratic Geo^{X}/G/1 Retrial Queue under J-vacation scheme using Nature Inspired Algorithms  
  by Radhika Agarwal, Shweta Upadhyaya, Divya Agarwal, Sumit Kumar 
  Abstract: In this article, we have explored a GeoX/G/1 model with Bernoulli feedback wherein the clients that enter and find the system to be busy, halt for a while prior to attempting again to enter the system. The server is erratic and can take utmost J-vacations regularly unless one client appears in the virtual track (orbit) again on returning from vacation. Also, the server is sent for repair on an urgent basis as soon as it breaks down. Using the probability generating function technique, the system size distribution of the server during busy, breakdown, vacation state and orbit size along with some performance measures have been derived. These derived quotients are then visualised and validated with the help of tables and graphs. Further, the cost analysis of the model is carried out and the optimal cost for the system is obtained. We have used direct search method, particle swarm optimisation (PSO), artificial bee colony (ABC) and cuckoo search (CS) techniques for the comparative study and presented the graphs for the convergence of these techniques.
  Keywords: discrete-time; starting failure; normal breakdown; J-vacation; Bernoulli feedback; cost optimisation; direct search; particle swarm optimisation; PSO.
  DOI: 10.1504/IJISE.2021.10041559
   
  
• Toward Smart Manufacturing Systems incorporating Reconfiguration Issues  
  by Ibrahim H. Garbie, Abdelrahman I. Garbie 
  Abstract: Nowadays, Industry 4.0 will become urgent to be implemented in most of the developed countries, although it is still mainly conceptually. As there are three different aspects consisting of Industry 4.0 (I4.0) such as digital systems, biological systems, and physical systems, most of the research published works were focused on mainly the first one. The smart manufacturing system (SMS) is not an invention, although it is representing the heart of I4.0. The SMS is a rebirth of a new version and innovation of production systems taken into consideration reconfiguring the existing manufacturing systems through adding machines with sensors, actuators, and control architectures for achieving the ultimate goals of I4.0. There are many challenges when reconfiguring these systems as an essential requirement to implement I4.0, representing the degree of individual system complexity, reconfigurable machines, material handling systems, system layout; competitive manufacturing strategies; and leanness agility), and embedded systems (cyber-physical systems). In this paper, a new perspective of reconfiguring manufacturing systems will be figured out, and the reconfigurability level toward I4.0 will be presented.
  Keywords: Industry 4.0; smart manufacturing systems; SMSs; reconfiguration.
  DOI: 10.1504/IJISE.2021.10041796
   
  
• An aircraft position updating based algorithm for single runway scheduling with normal and alternate aircrafts  
  by Hong-Da Dou, Feng Wang, He Pan, Yi-Fan Wang, Tsui-Ping Chung 
  Abstract: This paper investigates the problem of scheduling normal and alternate landing aircrafts at a single runway on Changchun Longjia International Airport. Usually, if the destination airport does not satisfy the landing conditions, then the aircraft has to use an alternate airport. Both normal and alternate landing aircrafts arrive at a fixed time window. Meanwhile, safety interval of adjacent landing aircrafts depends on their sizes. An integer programming model is proposed to minimise the landing completion time. Since the problem is NP-hard, an aircraft position updating based algorithm is proposed. To evaluate the performance of the proposed algorithm, a real case from Changchun Longjia International Airport and randomly generated problem instances are tested. The results show that the proposed algorithm has a better performance than the first-come first-served order and the landing constraints-based heuristic algorithms.
  Keywords: normal landing aircrafts; alternate landing aircrafts; single runway; fixed time window; safety interval; landing completion times.
  DOI: 10.1504/IJISE.2021.10041834
   
  
• Investigating the challenges faced by Indian Automotive Industry for adopting technology organically  
  by Mudita Dixit, Gopakumaran Thampi 
  Abstract: The Indian automobile sector is the sixth-largest producer of automobiles globally in terms of worth and volume. India has a steady trade deficit of US$ 2 billion in auto components every year. This paper critically examined reasons for India lagging in technology adoption and transfer organically in different sectors (OEM, large, medium and small enterprises) of the automotive industry (AI). A survey was conducted on 272 enterprises located in the western region of India. The result shows that the high purchasing cost of technology, lack of awareness of IT tools, availability and retainability of skilled workforce are critical issues for small and medium enterprises compared to large enterprises and OEMs. This study determines the role of government policies, state policies, and proactive measures to contribute to AIs fortune. It is observed that AI shall replicate success stories of the Indian IT industry in terms of global reach and quality arbitrage offering to the export market.
  Keywords: advanced manufacturing technology; R&D; Indian automotive sector; latest technology adoption; original equipment manufacturer; OEM.
  DOI: 10.1504/IJISE.2021.10041846
   
  
• Implementing Lean-Kaizen for Manufacturing Operations Improvement: A Case-Study in Plastics Industry  
  by Tareq Issa 
  Abstract: Lean manufacturing is concerned with the implementation of several tools and techniques that aim for the continuous elimination of waste in order to achieve competitive production systems. This research addresses the implementation of lean-kaizen concept and related techniques as part of a framework to achieve lean operation in a small-medium sized plastic bag manufacturing enterprise. The primary goal is to implement the lean-kaizen methodology to eliminate/reduce cycle time waste for the material mixing and roll formation processes in the manufacturing operation under study. The current state map was constructed, the processes identified for cycle time reductions were considered as well as the future state map was developed that served as a guide for lean-kaizen implementation. Root causes of waste were identified and two kaizen events were proposed as solutions. In the first kaizen event, the poka-yoke technique was used to automate the mixing process and eliminate variation and, for the second kaizen event, process standardisation was achieved in the roll formation process. As a result of implementing kaizen events, total cycle time was reduced and, consequently, productivity performance has increased to 94.7%.
  Keywords: lean-kaizen concept; kaizen event; cycle-time reduction; plastic bags industry; value stream map.
  DOI: 10.1504/IJISE.2021.10042029
   
  
• A Tutorial on Optimization involving David Ricardo Theory on Comparative Advantage  
  by Tapan P. Bagchi, R.P. Mohanty, Surajit Sinha 
  Abstract: Ricardo (1821) showed how two countries producing two different goods using a single endowed factor of production (the 2-2-1 situation), but operating with unequal efficiency, can benefit if they freely barter certain parts of their production, even if one is more efficient in producing every good. When done, such trade produces more goods in total using the same amount of total resource, rather than each producing enough goods only for own consumption, as in autarky. Ricardo showed that global benefits (measured in units of total goods produced) can accrue if each country specialises
  Keywords: comparative advantage; factor of production; free trade; linear programming; international trade; optimisation; Ricardo’s principle of trade.
  DOI: 10.1504/IJISE.2021.10042185
   
  
• Prediction of uncertainty risk factors in engineering management system based on improved decision tree  
  by Rong Tang, Guoxiong Zhang, Yunxia Li 
  Abstract: In order to overcome the problem of low efficiency of the current prediction method for uncertainty risk factors in engineering management system, this paper proposes a prediction method for uncertainty risk factors in engineering management system based on improved decision tree. In this method, the reason model (accident causal model of complex system) and software, hardware, environment and livewar (SHEL) model are used to analyse the uncertainty risk factors in engineering management system, and the prediction system of uncertainty risk factors is established. The fuzzy clustering analysis method is used to judge the expert weight of risk factors, and the improved decision tree algorithm combined with the judgment results is used to predict the uncertainty risk factors in engineering management system. The simulation results show that the proposed method can reduce the prediction error rate by 1.5% in the following time.
  Keywords: engineering management system; uncertainty; risk factors; improved decision tree; fuzzy clustering; prediction.
  DOI: 10.1504/IJISE.2021.10042297
   
  
• Use of heuristic methods for the optimization of truck loading in a steel company  
  by Andre Luis Korzenowsk, Felipe Kirsch Hoerbe, Taciana Mareth, Lucas Schmidt Goecks 
  Abstract: The correct layout of goods, objects or cargo, in the container’s available space is considered a complex task. The study was motivated by the need to implement a solution to optimise container use in a steel industry company in the South of Brazil. This article has contributed to synthesising research on the three-dimensional container loading problem, highlighting classifications, constraints, and algorithms used in its resolution. A framework is presented and may be used as a road map for practical implementation as used in this research. As a practical contribution, this article presents several instances of one actual case application. Results showed reducing of formatting loads processing time in comparison with the traditional company approach.
  Keywords: operational research; three-dimensional; container loading problem; CLP; steel industry.
  DOI: 10.1504/IJISE.2021.10042608
   
  
• An Innovation Ontology for Idea Forecasting and Measurement  
  by Andrew N. Forde, Mark Fox 
  Abstract: Before managers are able to forecast the utility of an idea, there must be a common definition and basis for measuring the potential radicalness of an idea. In this paper, we introduce an ontology to represent an innovation and derive properties that can be used to define and measure an ideas potential to be classified as a radical or incremental innovation. Our proposed ontology captures the concepts of an incremental or radical innovation, and further concepts to support the grouping of innovations. We begin with an extensive review of the literature and identify the categories of innovation, from this group we apply competency questions that allow us to define properties that are the basis for valuing an ideas utility, and classifying an innovation.
  Keywords: innovation management; ontology; semantic web; open innovation; radicalness; incrementalness; innovation properties; innovation categories.
  DOI: 10.1504/IJISE.2021.10042662
   
  
• Inventory management of manufacturers with yield uncertainty and lateral transshipment  
  by Arash Ashjaee, Mohammadali Pirayesh, Farzad Dehghanian 
  Abstract: This article deals with the issue of inventory management of one identical product in a manufacturers network. Manufacturers use lateral transshipments between each other in response to uncertainties in yield and demand to maximise the total profit. The demand of each manufacturer is considered random as a non-identical continuous probability distribution and their corresponding yield follows some possible scenarios. The objective of our model is to determine the optimal production amount and lateral transshipments in order to maximise the total profit considering the proceeds from sale of goods and salvage of remaining product and the cost of production, lateral transshipments, and shortages. The problem is modelled as a nonlinear constrained programming and the optimal solution is obtained by Karush-Kuhn-Tucker approach. Sensitivity analysis of uncertainty parameters based on a numerical example showed that the utility of using lateral transshipment policy increases with increasing the uncertainty in production yield.
  Keywords: inventory management; yield uncertainty; lateral transshipment.
  DOI: 10.1504/IJISE.2021.10042971
   
  
• Risk Warning Method of Computerized Accounting Information Distortion Based on Deep Integration Model  
  by Wenyuan Chen 
  Abstract: In order to improve the early warning accuracy of accounting information distortion risk and reduce the resource occupancy rate in the early warning process, this paper designs a deep integrated model-based computerised accounting information distortion risk early warning method. The distortion risk identification model is constructed to avoid the interference of invalid information and reduce the resource occupancy rate. Then the quantitative index is used to improve its effectiveness and improve the accuracy of the subsequent warning. Then the deep integration model is used to judge whether there is distortion node in the current computerised accounting information, so as to complete the high precision early warning of distortion risk. Simulation results show that the warning accuracy of this method is always above 0.9, and the resource occupancy rate of the warning process is less than 40%, which proves that this method achieves the design expectation.
  Keywords: computerised accounting information; index quantification; distortion risk identification; risk warning; deep integration model.
  DOI: 10.1504/IJISE.2021.10043036
   
  
• Integrated Scheduling and Vehicle Routing at Cross-dock Distribution Centre: A Simulated Annealing Approach  
  by Shikhar Saxena, Rajesh Piplani 
  Abstract: Cross-docking is a popular strategy for distributing products with short shelf-life that must be delivered within their pre-specified time windows to customers. Cross-docks receive shipments from suppliers which are stored in a temporary storage area before being consolidated and transferred to outbound vehicles for delivery to customers. This research tackles the joint problems of vehicle routing and scheduling at the cross-dock, along with product consolidation, by means of a mixed-integer programming model with the objective of minimising the total cost of operations. Our approach does not pre-cluster customers into zones and allows vehicles to deliver in less than truckload. To solve real-life sized problems, we develop simulated annealing algorithms which can solve the instances in 2 to 3 hours, achieving close to optimal solutions, making them suitable for decision support at cross-dock distribution centres, which process dozens of vehicles and deliver to hundreds of customers daily.
  Keywords: cross-docking; routing and scheduling; delivery window; meta-heuristic; product consolidation; simulated annealing.
  DOI: 10.1504/IJISE.2021.10043156
   
  
• Copper Futures Hedging based on Markov switching approach  
  by Jiaxuan Chen 
  Abstract: This paper selects the daily closing spot and futures prices of copper in China’s market from May 5, 1995 to February 28, 2020, and then proposes a two-regime bivariate Markov regime-switching model, DCC-GARCH, CCC-GARCH and the OLS model to estimate their time-varying minimum variance hedging ratio and hedging performance for comparison both in- and out-of-sample. The empirical results show that, whether in- or out-of-sample, the two-regime bivariate Markov regime-switching model can provide more detail depiction of dynamic correlation between spot and futures, and outperforms the others for hedging performance. Next is the DCC-GARCH model. CCC-GARCH model and the OLS model have similar performance. Besides, the rolling-window method can make the changes more obvious in the correlation of financial assets, which helps to estimate the time-varying optimal hedging ratio in the fast-changing market.
  Keywords: dynamic futures hedging; Markov regime-switching model; DCC-GARCH.
  DOI: 10.1504/IJISE.2021.10043159
   
  
• New mechanism of credit risk control in order agriculture  
  by Dongwei Shi 
  Abstract: The bilateral default rate of farmers and companies is usually high in contract farming. Inspired by the rule of
  Keywords: contract farming; bilateral default risk; mark-to-market.
  DOI: 10.1504/IJISE.2021.10043399
   
  
• IoT enabled smart window for controlling brightness: - A perspective of heat transfer rate  
  by Awais Kazi, Nikhil Shinde, Sumeet Mujumdar, Tejas Kulkarni, Prathamesh Potdar 
  Abstract: In a competitive environment, organisations are focusing on the energy-efficient smart system to reduce the expenses related to energy consumption and a comprehensive literature survey shows that windows are significant sources of heat and light in an enclosed space, which increases the load on air conditioner systems to maintain the comfortable conditions inside the room. There is a need to develop IoT enabled smart window for controlling heat and light in this context. In this study, suitable devices and sensors are identified based on a systematic literature survey to develop the IoT-enabled smart window. The experimental setup is also developed to evaluate the heat flow and luminosity inside the closed room. It has been observed that the maximum temperature recorded in the room in the range of 29
  Keywords: polymer dispersed liquid crystal; PDLC; internet of things; IoT; smart window; heating; ventilation; and air conditioning; HVAC.
  DOI: 10.1504/IJISE.2021.10043481
   
  
• A STUDY ON WORK-RELATED MUSCULOSKELETAL COMPLAINT AND ASSOCIATED RISK FACTORS AMONG STEEL INDUSTRIAL WORKERS  
  by Samson Akindele, Olusegun Akanbi, Feyisayo Akinwande, Joshua Ade-Omowaye 
  Abstract: As a result of scarce information regarding the impact of work-related musculoskeletal complaints (WMSCs) in the Nigerian steel industry, this research investigates the frequency of complaints in the various body regions. Subsequently, the relationship between WMSC and the essential worker’s characteristics (age, work tenure and weight) and working posture were addressed. The frequency of complaints of the working population was collected and accessed using the Nordic musculoskeletal questionnaire (NMQ). The active stance of the workers was analysed using the rapid upper limb assessment (RULA). The results from NMQ showed a significant relationship between complaints of the upper and lower back regions among the workgroups. Significantly, there exists a strong correlation among workers characteristics with WMSC. Older workers complained more about specific body regions than the relatively younger workers. The RULA showed that the maintenance department workers had the most significant postural risk, followed by the melting section.
  Keywords: posture; casting; productivity; rapid upper limb assessment; RULA; musculoskeletal complaints; discomfort; safety; steel industry.
  DOI: 10.1504/IJISE.2021.10043568
   
  
• A Case Study on the Design and Implementation of a New Product for Infants Learning to Walk  
  by Hu Shan, Jia Qi, Wang Yuqing, Fu Kaijie, Zhang Liyan, Guo Min, Guo Weiqi 
  Abstract: In order to solve the problem of the poor user experience and low satisfaction of infants and parents caused by insufficient research into existing products for toddlers. Based on the design and development process, this research takes pre-existing dual user research as its core and uses literature research, a focus group and other methods to determine dual user needs, as well as the Kano model to determine the demand attribute classification of the mixed methods of qualitative and quantitative research. Then, in this research, we design a system to help infants learn to walk that conforms to the characteristics of an infant’s physical and psychological development and meets the needs of parent users. The system can guide an infant to actively learn to walk through a multisensory interactive approach; meanwhile, parents’ fatigue and anxiety regarding children walking will be relieved during this period. In the final stage of this research, we design a product prototype to test the usability of the system. The research method can also be applied to other types of product design, and the design cue map obtained through user research has reference significance for other infant products.
  Keywords: infant; toddler; product design; user research.
  DOI: 10.1504/IJISE.2021.10043976
   
  
• Asymptotic analysis of a Bernoulli Vacation non markovian Queuing system in Air traffic control system  
  by Radha S, S. Maragathasundari, P. Manikandan 
  Abstract: We examine a single server queue arriving with Poisson batches of varying sizes. When the system starts the service, it provides service to all the arriving customers on a first come first served basis. Before the first service starts after each system downtime, the server provides general services to the client for a specified time of random duration, known to be a set-up time stage. If the server is affected by random crashes, a delay time occurs before the commencement of repair process. If there are no clients in the queue after the service is complete, the server takes a Bernoulli vacation. Two new parameters, reneging and restricted admissibility happen during the process of vacation and repair process respectively. For the defined queuing issue, we find the length of the duration of the steady state of different states of the system according to the probability generating function. Other queue performance metrics are also exported. In addition, the disposal is legalised through a digital scheme and graphic representation. This model means that supervisors are aware of the structural difficulties of the client-server framework and the basic rules of investigation.
  Keywords: setup time; service interruption; repair process; restricted admissibility; Bernoulli schedule; reneging; supplementary variable technique.
  DOI: 10.1504/IJISE.2021.10043978
   
  
• An integrated Fuzzy QFD approach to leagile supply chain assessment during COVID-19 crisis  
  by Fadoua Tamtam, Amina Tourabi 
  Abstract: The COVID-19 crisis has severely disrupted the Moroccan automotive production. This pandemic has weakened automotive supply chain; it faced a fall in demand and reduction in sales. Consequently, the automotive industry developed their production capabilities through constant innovation in resource reduction (leanness) while responding rapidly to demand changes (agility). A combination of lean-agile supply chain leads to obtain competitiveness in a time and cost effective manner. Successful implementation of leagile supply chain requires evaluation of criteria and attributes. To this end, the purpose of this paper is to propose a leagility evaluation framework using fuzzy quality function deployment approach. As a result, order guidance has been taken as the most important capability of automotive supply chain. E-fulfilment logistic has been considered as the most important enabler to gain supply chain leagility.
  Keywords: supply chain leagility; automotive industry; fuzzy quality function deployment; FQFD; leagile drivers; leagile capabilities; leagile enablers.
  DOI: 10.1504/IJISE.2021.10044277
   
  
• New approaches for the Prize-Collecting Covering Tour problem  
  by FRANCISCO CLIMACO, Luidi Simonetti, Isabel Rosseti, Pedro Henrique González 
  Abstract: In this paper, we consider the prise-collecting covering tour problem (PCCTP), which intends to find a minimum cost tour for travelling teams that grant assistance to people located far from urban centres. We develop a branch-and-cut algorithm, some valid inequalities, and a new set of reduction rules as exact approaches. We also present a hybrid heuristic that combines a state-of-the-art heuristic for the PCCTP with integer programming techniques. Computational experiments showed that the exact approaches found several new optimal solutions while reducing CPU time, and the hybrid heuristic was able to match or improve the solution quality for many instances, along with a significant reduction of running time.
  Keywords: prise-collecting covering tour problem; PCCTP; greedy randomised adaptive search procedure; GRASP; random variable neighbourhood descent; RVND; hybrid heuristic; reduction rules.
  DOI: 10.1504/IJISE.2021.10044534
   
  
• An Artificial Immune System Algorithm for Solving Stochastic Multi-Manned Assembly Line Balancing Problem  
  by Mohamamd Zakaria, Hegazy Zaher, Naglaa Ragaa 
  Abstract: In recent years, there has been an increasing interest in the multi-manned assembly line balancing problem (MALBP). It introduces the concept of assigning more operators at the same station to minimise the line length and to increase the production rate. Most of the previous works did not discuss such problems under uncertainty. Therefore, this paper presents a chance-constrained programming model that considers the processing times of the tasks as normally distributed random variables with known means and variances. The proposed algorithm for solving the problem is an artificial immune system algorithm. To get optimised results from the proposed algorithm, the parameters are tuned using a design of experiments. The computational results show the implementation of the proposed algorithm on 70 problems taken from well-known benchmarks in case that chance probability is equal to 0.95, 0.95, and 0.975.
  Keywords: MALBP; chance-constrained programming; artificial immune system; AIS; Taguchi orthogonal arrays; analysis of variance; ANOVA; Tukey’s HSD test.
  DOI: 10.1504/IJISE.2021.10044535
   
  
• An evolutionary game model for low-carbon technology adoption by rival manufacturers  
  by Yuxiang Yang, Ying Xie 
  Abstract: Manufacturers’ decisions on adopting low carbon technology are influenced by many factors, including the consumers’ awareness of low carbon technology and governmental carbon tax scheme. In this research, we considered competition between two rival manufacturers and constructed a demand function that considers carbon emission and price as parameters rather than constraints. We developed an evolutionary game model in bounded rationality space and analysed the game between two manufacturers under four game scenarios. The impacts of consumers’ awareness of low-carbon technologies and governmental carbon tax scheme were clearly demonstrated in the manufacturers’ behaviour strategies towards the adoption of low carbon technology. The research findings offered insights into the level of consumers’ low carbon awareness that stimulates both manufactures to adopt low carbon technology, and the threshold of low carbon awareness that incentivises only one manufacturer to adopt low carbon technology. Meanwhile, authority should enact carbon tax within appropriate range in order to reduce carbon emissions.
  Keywords: low carbon technology; evolutionary game; low carbon awareness; carbon tax.
  DOI: 10.1504/IJISE.2021.10044582
   
  
• Integrated optimisation of the unequal-area facility layout and the flowshop group scheduling problems for a case of the garment industry  
  by Sebastian Cáceres-Gelvez, Martín Darío Arango-Serna, Julian Zapata 
  Abstract: The unequal-area facility layout (UAFLP) and the flowshop group scheduling (FSGSP) problems are two important problems in both research literature and industrial applications. The former considers the location of departments with different area requirements within a floor plan. The latter seeks for a sequence of product families and jobs to be processed on groups of machines, called manufacturing cells. In this paper, an integrated approach for optimising both the UAFLP and the FSGSP is presented in the case of a sportswear manufacturing company. A genetic algorithm (GA) is proposed for minimising the sum of the total material handling and the tardiness costs. The results showed that the optimisation process obtained a reduction of 6.69% of the total costs for the proposed alternative, in comparison with the current situation of the case study.
  Keywords: unequal-area facility layout; flowshop group scheduling; genetic algorithm; garment industry; integrated optimisation; case study.
  DOI: 10.1504/IJISE.2021.10044730
   
  
• A Bibliographic Study of Sustainability Research: Exploring Multidimensionality  
  by Soumyanath Chatterjee, R.P. Mohanty 
  Abstract: Sustainability has gained prominence as a discipline for academics and professional practitioners. This article presents a bibliographic account and related analysis of research in sustainability between 1990 and 2019. A critical study of different aspects of sustainability requires a multi/interdisciplinary systems approach. Such a study may encompass ecological, economical, and sociological perspectives. For this reason, the bibliometric analysis has covered a wide range of professional disciplines. 183,779 bibliographic entries from SCOPUS were analysed with latent Dirichlet allocation (LDA) to discover different aspects of publications in sustainability. The study showed that all publications can be classified according to 25 topics, showing how sustainability research has evolved and the consequent gaps that need to be filled for the advancement of the research and community of practice. The LDA analysis resulted in creating a topic model that facilitates the automated categorisation of publications regarding sustainability.
  Keywords: sustainability; systematic literature survey; text analytics; latent Dirichlet allocation; LDA; topic model; bibliographic analysis.
  DOI: 10.1504/IJISE.2021.10044764
   
  
• Performance Improvement: A Lean Manufacturing Case of Metal Tools Factory  
  by Angassu Girma Mullisa, Walid Abdul-Kader 
  Abstract: Lean manufacturing in small and medium enterprises (SMEs) and ultimately success from implementation is marginal as compared to large enterprises. Poor Lean implementation technique and understanding is cited as one of the prominent reasons for the low success. Towards bridging this gap, a methodology utilising proper Lean diagnostic tools that identify waste and selection of relevant Lean tools for future state improvement works is conducted. To further validate the improvement recommendations, the use of discrete-event simulation (DES) is integrated with value stream mapping (VSM) to analyse the effects of improvement measures. A case study was addressed in an SME to improve the production performance and has led to reducing production Lead times by 58.5%, increasing process efficiency by 141.28% and cutting manufacturing cost by 51.7%. The research assists decision makers in SMEs that are interested in implementing Lean for improving their production performances.
  Keywords: value stream mapping; lean manufacturing; discrete event simulation; production performances.
  DOI: 10.1504/IJISE.2021.10045253
   
  
• Multi Objective Fuzzy Transportation Problem with Fuzzy Decision variables- NSGA-II Approach  
  by Admasu Tadesse, Srikumar Acharya, Manoranjan Sahoo 
  Abstract: In this paper, we consider a multi-objective fuzzy transportation problem with a fuzzy decision variable, with main objective and constraint parameters (supply and demand) considered to be triangular fuzzy numbers. Ranking function is used to convert fuzziness of objective and constraint functions into their equivalent crisp form.The crisp multi-objective transportation problem is solved using the non-dominated sorting genetic algorithm-II (NSGA-II), which is coded in MATLAB. A case study is provided to illustrate the methodology.
  Keywords: multi-objective programming; triangular fuzzy numbers; fuzzy transportation problem; fuzzy decision variables; ranking function; non-dominated sorting genetic algorithm-II; NSGA-II.
  DOI: 10.1504/IJISE.2022.10046605
   
  
• Analysis of Situation Awareness-Related Incidents in the Food Manufacturing Industry  
  by Griffin Wilson, Richard Stone, Kristina Schaffhausen 
  Abstract: A situation awareness (SA) oriented perspective on user errors and safety incidents has been widely used within aviation but seen almost no use in the manufacturing setting. This study was conducted with the objective of determining the prevalence of SA-related incidents in the food manufacturing industry. Incident investigations produced over a 72-week period from 37 different food manufacturing plants of one large multinational food producer were reviewed and categorised according to their SA-relatedness, the level of SA error occurring in each SA-related incident, and each SA errors’ relation to Endsley’s taxonomy of SA errors. The relationship between SA errors and amputations, life-altering and potentially life-altering incidents, and lockout-tagout violations is also analysed and discussed. We argue that taking this approach may reveal some novel design and training strategies which may reduce the occurrence of SA-related safety incidents in manufacturing.
  Keywords: safety engineering; situation awareness; cognitive engineering; food manufacturing; ergonomics; user-centred design.
  DOI: 10.1504/IJISE.2022.10046606
   
  
• Fear of COVID-19 Outbreak, Stress and Anxiety among working employees: A Multi-Service Sector Study  
  by Pratima Verma, Sumanjeet Singh, Vimal Kumar, Minakshi Paliwal, Preeti Sharma, Sung Chi Hsu 
  Abstract: The purpose of this study is to look at the link among financial stress, psychological stress, fear factors, and anxiety in the service industry as a result of the worldwide coronavirus epidemic. Additionally, the study also identified the various fear factors due to COVID-19. Regression analysis was applied to examine the responses of 539 service sector employees in India. The results revealed that the hypothesised variables’ connections had substantial effects. Through the numerous fear variables, this study gives vital insights into the impact of epidemics on diverse service industries. Based on the demographic analysis, this study revealed that employees of every service sector organisation had a different level of fear factors. The study assists managers and human resource practitioners in developing an action plan for the period leading up to and following COVID-19, as well as communicating with their employees, which includes managers, human resource practitioners, health and government officials.
  Keywords: COVID-19; psychological stress; financial stress; anxiety; fear factors.
  DOI: 10.1504/IJISE.2022.10046612
   
  
• An Optimization Approach for Multi-floor Facility Layout Design Using Flexible Bays  
  by Forough Enayaty-Ahangar, Behrooz Karimi, Negin Enayaty Ahangar, Alireza Sheikh-Zadeh 
  Abstract: We present the problem of optimising a multi-floor facility layout using the flexible bay structure that assigns block-shaped departments in parallel bays. A mixed-integer linear programming formulation is proposed to solve this problem. The decisions are determining: 1) rectangular land dimensions; 2) the number of floors; 3) each floor’s layout with the bay structure. The proposed formulation minimises the total cost associated with the layout that includes land cost, floor construction cost, elevator installation cost, and material handling cost within and among floors. To address the challenge inherited from the problem’s combinatorial dynamics, we develop a genetic algorithm utilising novel crossovers and mutations. The model and the solution approach are tested on a suite of problems from the literature. Our computational results verify the model and demonstrate that the solution approach is able to find high-quality solutions for large-scale problems in less computational time compared to the standard software.
  Keywords: facility layout; optimisation; mixed-integer linear programming; metaheuristics; genetic algorithm.
  DOI: 10.1504/IJISE.2021.10046616
   
  
• Measuring Warehouse Performance: A Systematic Literature Review  
  by Ayoub Ghaouta, Ahmed Ouiddad, Chafik OKAR 
  Abstract: Recently, there has been a huge of academic interest and publications in the area of warehouse performance (WP). This can be partly explained by the growing interest giving to warehouse performance (WP) in a wide variety of industrial sectors. In this context, this paper provides an overview of the methods in use in warehouse performance measurement (WPM) using a systematic literature review (SLR) which based on the principles of rigor, transparency and replicability required by the methodology. This review paper describes the budding area of WPM, provides an overview of warehouse performance measures/criteria/techniques and develops an architectural framework. The framework enables researchers to seek fundamental knowledge and pursue further research regarding WPM. This study also provides practical value by offering a guidance for decision makers considering the trade-off among different warehouse processes and performance measurement (PM). Findings disclose that PM in WM contexts is still a productive area of research.
  Keywords: key performance indicators; logistics; systematic literature review; SLR; warehouse management.
  DOI: 10.1504/IJISE.2022.10046645
   
  
• Process Improvement Using Six-Sigma DMAIC in Bearing Component Manufacturing Industry: A Case Study  
  by Sandeep Kumar Bhaskar, Manoj Kumar Sain, Manu Augustine, Praveen Saraswat, Brij Mohan Sharma 
  Abstract: Bearing parts manufacturing process is highly crucial in maintaining the quality of final product. This paper elaborates the use of the Six Sigma DMAIC approach to minimise variation in dimensions of inner and outer races of ball bearings for enhancing process quality in a manufacturing firm. In various phases of DMAIC, Six Sigma quality improvement tools like voice of customers and employee, statistical process control, control charts, process capability charts, customer/employee survey, fishbone diagram were used. MINITAB18.0 software was used for data analysis. The results revealed that the adoption of the Six Sigma DMAIC approach significantly improved the process quality. Sigma level was improved from 2.5 to 3.8; defects per million opportunities were reduced from 0.632% to 0.023% and process variation was reduced from
  Keywords: process capability analysis; process capability indices; process improvement; quality improvement; Six Sigma DMAIC.
  DOI: 10.1504/IJISE.2021.10046658
   
  
• A Lion Optimization Algorithm for a Two-Agent Single-Machine Scheduling with Periodic Maintenance to Minimize the Sum of Maximum Earliness and Tardiness  
  by Reza Yazdani, Mirpouya Mirmozaffari, Elham Shadkam, Seyed Mohammad Khalili 
  Abstract: The multi-agent scheduling with periodic maintenance concerns has received little attention till recently. The focus of this research is on the single machine scheduling problem that the machine goes under periodic maintenance, for two agents with the aim of minimising the sum of maximum earliness and tardiness of jobs from the first agent, while ensuring that the sum of maximum earliness and tardiness of jobs from the second agent does not exceed an upper bound. For this NP-hard problem, the Lion optimisation algorithm is employed to find the optimal solutions. Experimental results show that the suggested lion optimisation algorithm outperforms dragonfly algorithm (DA), grasshopper optimisation algorithm (GOA), sine cosine algorithm (SCA) and Salp swarm algorithm (SSA) in computational and optimisation stability.
  Keywords: lion optimisation algorithm; LOA; multi-agent; maintenance; single machine; metaheuristic; grasshopper optimisation algorithm; GOA; sine cosine algorithm; SCA; Salp swarm algorithm; SSA.
  DOI: 10.1504/IJISE.2023.10047110
   
  
• The spillover effect of COVID-19 on US financial markets-based on MF-DCCA method  
  by Renzao Lin, Liang Ying, Zhe Wang 
  Abstract: This paper uses the S&P 500 (SPX.GI), the US dollar index (FTSE.GI) and the Libor interest rate to represent the US stock market, foreign exchange market and currency market respectively. The multifractal trend cross correlation analysis (MF-DCCA) method is used to study the influence of COVID-19 on the cross correlation between the three major financial markets in the USA. The results show that there are multifractal characteristics among US stock market, money market and foreign exchange market, which show the characteristics of persistence in small fluctuation and anti-persistence in large fluctuation. Moreover, the impact of COVID-19 has greatly affected the cross correlation between the multifractal characteristics of the three financial markets in the USA. The conclusions of this paper are helpful to sort out the nonlinear dependence and potential impact dynamic mechanism among the three major financial markets in the USA.
  Keywords: COVID-19; multifractal; detrended cross correlation; MF-DCCA.
  DOI: 10.1504/IJISE.2022.10047244
   
  
• Integrated production and non-cyclical maintenance planning in flow-shop environment with limited buffer  
  by Shahrzad Derakhshan, Mehdi Bijari 
  Abstract: In this paper, we work on a problem of determination of production and maintenance scheduling as well as the production lot sizes in flow-shop environment with limited buffer. We introduce a new mathematical model that can schedule production and non-cyclical maintenance activities. The objective of the problem is minimising the total costs consisting of production, setup, inventory and preventive maintenance costs. A method for linearising the mathematical model is introduced. Since the problem is NP-hard, solving the problem with proposed mathematical model in large and medium sizes is hard and time consuming. Hence, two heuristic algorithms based on fix and optimise approach are developed. For checking the solution quality, the numerical results of heuristic algorithms are compared with optimal solution, lower bound value and the best answer that gained among the examples.
  Keywords: scheduling; lot-sizing; non-cyclical preventive maintenance; limited buffer; fix and optimise algorithm.
  DOI: 10.1504/IJISE.2022.10047246
   
  
• Availability optimization of Heat Treatment Process using Particle Swarm optimization approach  
  by Ajay Kumar, Devender Punia 
  Abstract: In this research paper a methodology is presented for prediction of performance parameters of a series parallel industrial system. The particle swarm optimisation (PSO) technique is used for evaluating the performance of industrial system and the Markov method is used for mathematical modelling. The mean time to failure is calculated to be 352 days and it is observed that after 30 days the reliability of the system became steady state which shows the bathtub behaviour. Using the PSO technique for maximising the system availability (SA) with ranges of performance parameters selected from the real industrial system, the different economical possible performance measures for maximum availability is predicted which are helpful for reduction in cost of production. From the performance analysis the optimised availability using PSO is estimated 94.25% whereas it is 93.60% using Markov method.
  Keywords: availability; steady state analysis; SSA; particle swarm optimisation; PSO; reliability; transient state analysis; TSA.
  DOI: 10.1504/IJISE.2022.10047431
   
  
• Decision Making Support System for Medical Devices Maintenance Using Artificial Neuro Fuzzy Inference System  
  by Akram Alsukker, Nour Afiouni, Morad Etier, Mohannad Jreissat 
  Abstract: Reliable and successful maintenance management system is needed to achieve the best system with lowest costs. The lack of proper medical devices maintenance management in healthcare facilities is leading to unreliable usage of medical devices. This study focused on the decision making process of maintenance of medical devices. Each device was classified according to multiple factors, such as their function, age, price, risk, availability, and utilisation. Artificial neuro fuzzy inference system (ANFIS) was used to choose the best maintenance strategy and compared to neural networks, fuzzy inference system (FIS), and linear regression. Results showed that the best applied method was ANFIS using subtractive clustering in terms of testing data accuracy, with the highest accuracy of 82.99% compared to neural networks (78.16%) and ordinal logistic regression (73.47%). This study recommends incorporating ANFIS approach to healthcare facilities medical devices maintenance management leading to better healthcare services with minimum costs.
  Keywords: decision making; maintenance management; neural network; artificial neuro fuzzy inference system; ANFIS; medical devices.
  DOI: 10.1504/IJISE.2022.10047433
   
  
• Course Scheduling Problem with Cohort Group and Room Considerations  
  by Lijian Xiao, Pratik Parikh, Xinhui Zhang 
  Abstract: We consider a variation to the course scheduling problem (CSP), referred to as the course scheduling problem with cohort group and room considerations (CSP-CgR), that incorporates two considerations; cohort group-to-room and topic-to-room assignments. While the former refers to students in each group (within a cohort) staying in the same room for most of the week to reduce student travel distances, the latter ensures that topics will be assigned to preferred rooms. We first propose a mixed integer optimisation model and then develop a nested simulated annealing (SA) based algorithm to solve real-world instances. The algorithm utilises several neighbourhood operators and contains enhancements such as probability selection and candidate listing to guide the search process. Testing this algorithm on small and large problems instances (up to 450 students, ten cohorts, 16 groups and ten rooms) suggest that high quality solutions can be obtained in under four hours.
  Keywords: course scheduling; simulated annealing; multi-objective optimisation; room planning.
  DOI: 10.1504/IJISE.2022.10047812
   
  
• Analysis of construction supply chain critical success factors: A Multicriteria decision making approach  
  by Kenan Sarayji, Sharfuddin Ahmed Khan, Muhammad Shujaat Mubarik 
  Abstract: This study analyse the critical success factors (CSF’s) of construction supply chain management (CSCM) using a mix-method approach to identify and prioritise the CSF’s for CSCM performance. An extensive literature review was conducted to identify the potential CSF’s and with the help of experts, 21 relevant CSFs were selected. The analytical hierarchy process (AHP) was employed to prioritise the selected CSF’s. Results showed that the top management commitment, information sharing and flow and supply chain finance are highly ranked among all the factors. Research findings will help to minimise resource wastage on less essential things and increase efficiency. This research is unique from several perspectives. Firstly, this study is one of the first studies, which identify CSF of construction supply chain in the context of UAE. Secondly, this study provides several managerial and practical implications to managers to minimise resource wastage on less essential things and increase productivity and efficiency.
  Keywords: supply chain management; construction supply chain; critical success factors; multi-criteria decision making; MCDM; analytical hierarchal process; AHP.
  DOI: 10.1504/IJISE.2022.10047831
   
  
• Investment Efficiency Evaluation of Electric Power Substation Project by Stages Using the EWM  
  by Xu Ma, Zhenyu Zhao 
  Abstract: Power grid construction projects are crucial for national industrial development. To improve the investment efficiency (IE) of substation engineering construction, this study determines the influencing factors of the input and output of substation construction projects through the entropy weight method (EWM) principle and constructs the evaluation model of IE of substation engineering construction by using data envelopment analysis (DEA) theory. Considering substation projects of a power grid company as an example, this article evaluates and compares the investment economy of one-time and phased construction to maximise the economic efficiency of substation project construction. Hence, our study provides scientific reference for investment decision-making involving substation projects and promotes collaborative planning of various regions, projects, and assets under the construction of substation projects. The results show that the IE of phased substation construction projects is higher than that of one-time construction. This effectively improves the IE of power grid companies.
  Keywords: substation project; data envelopment analysis; DEA; entropy weight method; EWM; input–output index; construction by stages; investment efficiency; IE.
  DOI: 10.1504/IJISE.2022.10047832
   
  
• Study on Optimization of Supply Chain Inventory Management Based on Particle Swarm Optimization  
  by Shanyin Yao, Yehui Dong, Jiawei Gao, Minglei Song 
  Abstract: Aiming at the problems of poor convergence, high cost and low efficiency of traditional supply chain inventory management model, a supply chain inventory management optimisation method based on particle swarm optimisation (PSO) is proposed. Firstly, the whole process of particle swarm optimisation (PSO) is described. Secondly, by introducing the inventory of different nodes in the supply chain, the optimal inventory management model meeting the requirements of the supply chain model is designed. Finally, the particle swarm optimisation algorithm is used to design the optimal inventory management model and generate the optimal inventory. The experimental results show that the total inventory cost of this model is only 3.682 million Yuan, which is much lower than other traditional models. It shows that the model can effectively reduce the inventory management cost of supply chain, has high convergence, and can reduce the work intensity of relevant personnel.
  Keywords: particle swarm optimisation; PSO; supply chain; inventory; management; model.
  DOI: 10.1504/IJISE.2022.10048297
   
  
• A robust nonsingular fast terminal sliding mode controller for optimizing a wind energy process  
  by Dahech Karim, Moez Allouche, Tarak Damak 
  Abstract: This paper deals with the development of a non-singular fast terminal sliding mode controller for maximum power point tracking of wind energy conversion system. The studied system is made up of a wind turbine coupled to a permanent magnet synchronous generator, a three phase diode rectifier and supplying, through a boost converter, a resistive load. The principle of the control scheme is based on electrical measurements and the choice of a suitable sliding surface. This approach presents a good transition response, a low tracking error and a very fast reaction against wind speed variations compared to the traditional sliding mode controller. The effectiveness of the proposed control scheme is demonstrated by numerical simulations under different climatic conditions.
  Keywords: wind energy process; maximum power point tracking; robust control; non-singular fast terminal sliding mode controller; Lyapunov function.
  DOI: 10.1504/IJISE.2022.10048717
   
  
• Application of Cognitive Work Analysis in support of Systems Engineering of a Sociotechnical System  
  by Henk Van Den Heever, Rudolph Oosthuizen 
  Abstract: This paper presents a validation workflow to support system requirements analysis. Systems engineering supports the development of sociotechnical systems. However, the traditional systems engineering approach of reducing the system to component level to perform detailed designs and integrate them into a solution system may miss unexpected emergent behaviour when introducing a new technology into a socio-technical system. It may require changes in the socio-technical systems information flows, processes and procedures. Ignoring these emerging requirements may result in undesirable results or failures in the system. Cognitive work analysis, with work domain analysis in particular, provides a framework for analysing, modelling and designing socio-technical systems. The output abstraction hierarchy models were evaluated using a focus group approach for perceived utility in uncovering potential design emergence. The focus groups supported both the models and the proposed method. This structured approach will support requirements capturing and analysis for developing and engineering socio-technical systems.
  Keywords: cognitive work analysis; CWA; systems engineering; requirements analysis; work domain analysis; WDA; emergence; socio-technical.
  DOI: 10.1504/IJISE.2023.10048719
   
  
• A VNS-IG algorithm for dynamic seru scheduling problem with sequence-dependent setup time and resource constraints  
  by Yiran Xiang, Zhe Zhang, Xue Gong, Yong Yin 
  Abstract: This paper concerns with the unspecified dynamic scheduling problem by consideration of sequence-dependent setup time and resource constraints in the setups (UDSS-SR) in a new-type seru production system (SPS). The UDSS-SR problem is formulated as a mixed integer linear programming (MILP) model to minimise the makespan, and an iterative greedy algorithm based on variable neighbourhood search (VNS-IG) is designed subsequently to facilitate decision-making in the real environment to rationalise operations and additional resources. A set of test problems are generated, and computational experiments with different instance sizes are made finally. The results indicate that the proposed VNS-IG algorithm has good performance in solving seru scheduling problem in terms of solution quality and efficiency.
  Keywords: scheduling; seru production; sequence-dependent setup time; resource constraint; hybrid intelligent algorithm.
  DOI: 10.1504/IJISE.2022.10048721
   
  
• An-Optimization based System For The University Course Timetabling: A novel integer linear programming model  
  by Olfa Khlif, Jouhaina Chaouachi, Mahdi Mrad 
  Abstract: University course timetabling is an ongoing challenge that most of educational institutions face when scheduling courses. The problem assigns lectures to specific numbers of time slots and rooms while including several conflicting constraints into account. Hence, effective decision making is strongly required to provide the timetabler’s a useful toolkit. This article integrates a user friendly decision support system for the university course timetabling problem. The system employs a novel linear programming formulation that provides constraints for a great number of operational rules and requirements. Treated as an optimisation problem, the objective is to provide a best solution satisfying at most teachers’ preferences. The case of the High Institute of Business Studies of Carthage with a huge number of courses and teachers is treated along. The computational results of our system demonstrate a significant performance over the manual process on both computation time and solution quality.
  Keywords: course timetabling; teacher preference; decision support system; DSS; sum colouring problem; integer linear programming; ILP.
  DOI: 10.1504/IJISE.2022.10048765
   
  
• Research on the Design of Smart Sleep Aid Interac-tive Products  
  by Hu Shan, Zhang Liyan, Guo Weiqi, Dong Zhang, Jia Qi, Zitong Yang, Guo Min 
  Abstract: The purpose of this research is to solve their problems of being uncomfortable, easy to lose, and unable to accurately determine the user’s sleep state to cause second awakening, so as to maximise the needs of users and improve the user experience. The research uses the analytic hierarchy process (AHP), quality function deployment (QFD), and function behaviour structure (FBS) models to integrate an innovative design method. This method first extracts user needs and calculates weights through an AHP. The results use convolutional neural networks to build a user sleep state model with personal sleep characteristics to achieve effective transformation of user requirements to design results. This method can improve the user experience of intelligent sleep aid products from the perspective of user needs and provide a feasible reference for the design research of intelligent sleep aid products.
  Keywords: smart products; sleep aid; analytic hierarchy process; AHP; quality function deployment; QFD; function behaviour structure; FBS model; intelligent monitoring.
  DOI: 10.1504/IJISE.2022.10048766
   
  
• A semi-Markov decision model for the optimal control of an Emergency Medical Service System  
  by Giannis Kechagias, Alexandros Diamantidis, Theodosis Dimitrakos 
  Abstract: A mathematical model for the analysis of an emergency medical service (EMS) system with a specific number of advanced life support units (ALS) and a specific number of basic life support (BLS) units is presented in this paper. The system admits incoming emergency calls which are divided into two classes: 1) urgent, high-priority calls for which the patient’s life is potentially at risk; 2) less urgent low-priority calls. Under a suitable cost structure, the system is modelled using an appropriate Markov decision process in continuous time for which we seek a stationary policy that minimises a predefined optimality criterion for vehicle mixes over a set of candidate ambulance fleets. Based on this formulation, it is possible to implement standard Markov decision algorithms, such as the standard value-iteration algorithm and the standard policy-iteration algorithm. A sensitivity analysis of some model parameters is provided to examine their effect in the vehicle mix and in the cost of the system. An integer programming formulation is also provided for the corresponding location-allocation problem of the model. Numerical results are also presented for the examined problem.
  Keywords: emergency medical service system; EMS; vehicle mix; Markov decision process; MDP; integer programming.
  DOI: 10.1504/IJISE.2022.10049019
   
  
• New Order-picking Routing Heuristics for Single Block Rectangular Warehouse  
  by ?Hala Ahmed, Mahassen Khater, Raafat Elshaer 
  Abstract: Order picking is the most labour-intensive and costly activity for almost every warehouse. This paper has two main objectives: first, to propose two new routing heuristics for order-picking in a rectangular warehouse, Ascending and Ascending+, and second, to investigate the impact of order region on the performance of routing heuristics. Four experiments based on dividing the warehouse into twelve main regions and large numbers of randomly generated test problems are designed to validate the proposed heuristics’ performance. The computational results demonstrate the effectiveness and efficiency of the ascending+ heuristic compared to the performance of the previously published heuristics. The statistical analysis reveals the significant impact of the order region on the performance of the routing heuristics. Therefore, each order region is allocated to the best-performed heuristic.
  Keywords: order picking heuristics; warehouse; logistics.
  DOI: 10.1504/IJISE.2022.10049128
   
  
• Assessing Process Time Estimation for Job Sequencing in Moving Mixed-Model Assembly Lines  
  by Faisal Alfaiz, David Kim, Hector Vergara 
  Abstract: Job sequencing optimisation in moving mixed-model assembly lines has been studied extensively, and many optimisation procedures require average job processing times as input. However, in practice estimating average job processing times can be difficult for multiple reasons. In this study, various factors related to job processing time estimation are investigated to provide insight into more efficient job processing time estimation to support sequencing optimisation. To this end job sequencing optimisation was performed using job processing times representing estimates, where specific differences from assumed true average processing times were controlled, and separately with the assumed true average processing times. Experiments were conducted to identify the characteristics of the estimated processing times having the greatest impact on job sequence performance (i.e., the performance differences between using estimated, and assumed true processing times in job sequencing optimisation). The results indicate that if the estimated processing times used for job sequencing optimisation replicate specific properties (e.g., the rank) of true processing times, which may be different for different assembly systems, then most benefits of job sequencing optimisation may be realised.
  Keywords: sequencing; job sequencing; assembly lines; assessing processing time; mixed-model assembly lines; MMAL; processing time; processing time estimation.
  DOI: 10.1504/IJISE.2022.10049208
   
  
• NBSOC Framework for Team Structure to develop Blockchain-based Applications  
  by Nitish Joshi, Tejaswi Khanna, Vikram Bali, Shivani Bali 
  Abstract: For the development of blockchain applications, platforms have been implemented by corporates like IBM, Oracle, Amazon, etc. Blockchain comprises smart contract development, which gets deployed over a peer-to-peer network. Basic skills about blockchain still lack amongst the developer community and all the people involved in developing blockchain applications. This paper proposes an NBSOC framework for organising teams to build blockchain-based applications. This framework has been used to create a team structure for implementing a land record management system. The authors have addressed the implementation challenges, cost, roles and responsibilities of an individual in the blockchain development environment.
  Keywords: blockchain; team structure; smart contract; decentralised applications; software engineering.
  DOI: 10.1504/IJISE.2022.10049488
   
  
• MACHINE LEARNING FOR OPTIMIZATION OF FLOW-RACK AS/RS PERFORMANCES  
  by Zakaria Amara, Latefa Ghomri, Ali Rimouche 
  Abstract: In this paper, we are interested in flow-rack automated storage/retrieval systems (AS/RS), which are compact AS/RS. For this configuration of AS/RS we propose a new storage method based on machine learning (ML), i.e., ML method that assigns to each incoming load a position in the rack, in such a way, that the retrieval time of this same load will be optimal. In other words, we tidy out the loads inside the rack, In order to facilitate access to each type of loads. Consequently, the total (average) retrieval time in the system is minimised. The choice of ML is mainly due to the fact that the output, which is the minimisation of the average retrieval time, cannot be expressed as a function of the input, which is the choice of the most appropriate cell, for the storage of each incoming load. We compared the proposed model results with other basic storage methods. The obtained results were very satisfactory.
  Keywords: flow rack AS/RS; retrieval time prediction; supervised machine learning; regression; classification.
  DOI: 10.1504/IJISE.2022.10049510
   
  
• Active stabilization of seaports co-evolution system for port throughput with time delay  
  by Xiao Xu, Hwan-Seong Kim, Truong Ngoc Cuong, Sam-Sang You 
  Abstract: This paper aims to investigate co-evolution dynamics with decision making policy for seaport throughput subjected to the time-delayed interactions. To explore interaction relationships among seaports, dynamical behaviours of co-evolution system are demonstrated using Lotka-Volterra model. Due to the time delay of interactions, the co-evolution dynamics exhibits strong fluctuations and undesirable behaviours leading to system instabilities. Adaptive fractional order sliding mode control is implemented to achieve robust stabilisation of the nonlinear co-evolution system with time delay under disturbances. The numerical simulations are presented to validate the effectiveness of the proposed control algorithm. This study systematically explains how time delays in the supply chains affect seaport co-evolution behaviours for cargo throughput and how they can be actively managed by decision making strategy. The results reveal that the proposed methodology can provide a resilience strategy under market uncertainty. Finally, conclusions are made regarding the manageable side of the time-delay problems.
  Keywords: seaport co-evolution; port throughput; time-delay; Lotka-Volterra model; adaptive fractional order sliding mode.
  DOI: 10.1504/IJISE.2022.10049595
   
  
• Improving Forecast accuracy for Seasonal products in FMCG Industry: Integration of SARIMA and regression model  
  by Deepak Bartwal, Rohit Sindhwani, Omkarprasad S. Vaidya 
  Abstract: Increasing forecast accuracy of seasonal products is very critical as production; inventory and customer service depends on it. There has been introduction of new models, techniques and use of advance data analytics in forecasting, however, considering the complexity of the several causal variables and demand; it has been very difficult to get the consistent accuracy. This paper proposes integrated SARIMA (for non-seasonal component of demand) and regression (for seasonal component of demand) models for improving the forecasting accuracy. Further, we evaluate the performance of the proposed model with other known methods such as, SARIMA, ANN and SARIMAX. The performance is evaluated on various parameters of forecasting error. It is seen that for the empirical data, the proposed method outranks the other methods on all the performance metrics. Further, this paper brings into managerial insights, which can be replicated to various industries, indicating the wide scope of the proposed approach.
  Keywords: forecasting; insecticide; regression; SARIMA; seasonality.
  DOI: 10.1504/IJISE.2022.10049596
   
  
• Seru scheduling problems with learning effect and job deterioration during an increasing adjustment period  
  by Ru Zhang, Zhe Zhang, Xiaoling Song, Xiaofang Zhong, Yong Yin 
  Abstract: This paper focuses on seru scheduling problems during an increasing adjustment period considering learning effects and job deteriorations, in which the job’s processing time is defined by a function of job position in the processing sequence, adjustment position and effects of learning and deterioration. Each seru has an increasing adjustment period, which means that the later the adjustment, the longer the duration. Moreover, the seru will return to its original state and the deterioration effect will restart from new position after adjustment, yet the learning effect keeps growing. The objectives are to minimise the total seru loads (TSL), the total completion times (TC) and the total absolute deviation in completion times (TADC), respectively. A general exact solution method is proposed and optimal solutions for seru scheduling problems are obtained. The comprehensive experimental analysis is conducted, and the results demonstrate that the proposed method is able to return high-quality solutions for seru scheduling problems.
  Keywords: seru scheduling; learning effect; job deterioration; increasing adjustment period; exact solution method.
  DOI: 10.1504/IJISE.2022.10049678
   
  
• Analytical hierarchy process based Maintenance quality function deployment integrating Total quality management with Total productive maintenance and its application in dairy industry  
  by Jeffin Johnson, Pramod V. K, Pramod V.R. 
  Abstract: Quality enhancement of the products and services provided by manufacturing enterprises is gaining more and more attention from researchers. This research work leads to the development and implementation of AHP-based maintenance quality function deployment (MQFD) model in the dairy industry. It prioritises and identifies the prominent factors which are involved in the quality performance of the organisation. MQFD model is developed by combining TPM and TQM approaches. AHP was adopted for prioritising the decision alternatives on the basis of the main and sub-factors. Through the evaluation, the local sensitivity of critical factors such as increased profit and reliability of decisions were found to be 0.708 and 0.472, and global sensitivity of the factors such as quality of products and TQM tools were obtained as 0.351 and 0.252. The sensitivity analysis will help the organisation to find the optimum parameter in order to achieve its market goals.
  Keywords: analytical hierarchy process; AHP; quality function deployment; QFD; total productive maintenance; TPM; total quality management; TQM; maintenance quality function deployment; MQFD.
  DOI: 10.1504/IJISE.2022.10049797
   
  
• Exploring the Manufacturing Flexibility Issues to Build a Framework to Implement the Manufacturing Flexibility of a Supply Chain: A Review  
  by Chowdhury Jony Moin, Mohammad Iqbal, A.B.M. Abdul Malek, Muhshin Aziz Khan 
  Abstract: Manufacturing flexibility is considered one of the most in-demand properties for manufacturing firms in the present highly competitive markets and uncertain business environment. Implementation of manufacturing flexibility is also difficult. This study aimed to minimise the difficulties in understanding the manufacturing flexibility issues through a review of highly cited scientific articles. The period of the selected articles was from the foundation of the topic (1980) to up-to-date (2021). This study explored and organised manufacturing flexibility components; manufacturing flexibility types, their interrelationships, drivers, sources, and relationship with various exogenous and endogenous issues. Finally, the study suggested a generalised framework for implementing and managing manufacturing flexibility for a homogeneous industry which would be an easy and systematic approach for decision-makers. The study concluded that to implement the manufacturing flexibility, researchers and practitioners should take a single firm or homogeneous industries of a specific supply chain as an entity rather than heterogeneous industries.
  Keywords: manufacturing flexibility; environmental uncertainty; homogeneous industries.
  DOI: 10.1504/IJISE.2022.10049929
   
  
• Optimal Pricing Decisions in a Two echelon Green/non green Resilient Supply Chain for Substitute and Complementary Products Considering Disruption Risk  
  by Ashkan Mohsenzadeh Ledari, Alireza Arshadikhamseh 
  Abstract: In this paper, a pricing model is presented for substitute and complementary products, where the manufacturers 2 and 3 products are alternatives while manufacturer 1 produces a complimentary product for the others. The first manufacturer produces one green product that increases the tendency of customers for buying this product and its substitutes which brings more costs to the supply chain. Hence, the relationship between the manufacturers and the distributor is modelled by both cooperative and non-cooperative games. In the first model, the whole system works integrally, whereas, in the non-cooperative game, the model is analysed by the Stackelberg equilibrium where the manufacturers have disregarded leaders and the distributor is a follower. Moreover, potential disruption risks between the manufacturer and the distributor are considered in the current paper which means only a percentage of the distributor’s order quantity can be fulfilled by the manufacturer during disruption conditions. The optimal prices and the green degree for the products have been achieved parametrically using KKT conditions and finally, a numerical example is presented to describe the model.
  Keywords: green supply chain; GSC; pricing; green product; substitute product; complementary product; game theory; disruption risk.
  DOI: 10.1504/IJISE.2022.10049930
   
  
• A Quantitative Analysis of Simultaneous Supply and Demand Disruptions on a Multi-Echelon Supply Chain  
  by Austin R. Kost, Hector Vergara, David Porter 
  Abstract: This research aimed to uncover how different features of simultaneous supply and demand disruptions impact the performance of a multi-echelon supply chain. A discrete event simulation model was developed in ARENA and a full factorial designed experiment was conducted to understand how different disruption characteristics affect key supply chain performance metrics. Historical data was obtained for a four-echelon supply chain owned by a single company using the guaranteed service model inventory policy. Results showed that the severity of a demand disruption had considerable impact on performance during the disruption period. Furthermore, disruptions that occurred further upstream in the supply chain were more likely to translate into a decrease in overall performance in the post-disruption period when compared to disruptions located elsewhere. It was also found that additional inventory can be expected to accumulate at a disrupted node which, in turn, could translate into inventory reductions immediately upstream of the disrupted node.
  Keywords: discrete event simulation; guaranteed service model; GSM; supply chain disruption; SCD; mitigation strategies; multi-echelon supply chain.
  DOI: 10.1504/IJISE.2022.10050004
   
  
• Median and Interquartile Range Control Charts Based on Quantiles of Marshall-Olkin Inverse Log-logistic Distribution  
  by Olubisi L. Aako, Johnson A. Adewara, Kayode Sam Adekeye 
  Abstract: The presence of outliers makes process data deviate from normality and reduces the sensitivity of control charting procedures. This paper proposed a robust method of determining the control limits of and R charts in the presence of outliers when the data deviates from normality. The quantile of Marshall-Olkin inverse log-logistic distribution (MOILLD) is derived. The quantiles of the distribution are then used to estimate the process location and dispersion for the construction of control limits of median and interquartile range control charts. Control limit interval, false alarm rate, and average run length were used to compare the performance of the proposed control charts with similar control charts in the literature. The results showed that the proposed method detects out-of-control faster than the classical Shewhart control chart and robust control charts whose control limits were based on raw data.
  Keywords: control charts; interquartile range; inverse log-logistic distribution; median; quantiles; Marshall-Olkin inverse log-logistic distribution; MOILLD.
  DOI: 10.1504/IJISE.2022.10050090
   
  
• Development of a Face Shield Concept to Protect Against COVID-19 Infection using Integrated CAD and CAE Tools and Sustainable Design Techniques: Deployment of International Standards  
  by Nasser Ramsawak, Boppana V. Chowdary 
  Abstract: To this day, the COVID-19 pandemic has infected hundreds of millions of persons globally. Counter measures to combat this virus have been orchestrated by major health enterprises that have approved solutions including vaccines, social distancing and facial protection. As such, this paper focuses on the development of a COVID-19 preventative face shield concept using integrated computer-aided design and engineering (CAD and CAE or CAD/E) tools alongside sustainable design techniques to generate a virtual model in compliance with the safety standards as recommended by the major international health organisations. The study will employ an extensive review of literature, standard product development practices, CAD drawings, and CAE simulations and analysis to facilitate the concept’s evolution. This proposal can prove highly valuable to sanitation companies owing to the recently exorbitant market demands for face shields because of the pandemic, which can in turn provide substantial profit to both a business and daily consumer.
  Keywords: COVID-19; face shield concept; computer aided design; CAD; computer aided engineering; CAE; sustainable design techniques; safety standards; product development practices.
  DOI: 10.1504/IJISE.2022.10050343
   
  
• A Multi-Objective Optimisation for Green Supply Chain Network Design Problem Considering Economic and Environmental Sustainability  
  by Sreyneath Chhun, Saowanit Lekhavat, Mohammad Alghababsheh 
  Abstract: The aim of this study is to develop a multi-objective optimisation for the green supply chain network design (GSCND) problem considering economic and environmental sustainability. The economic and environmental sustainability of different facilities (i.e., suppliers, plants and distribution centres) and allocation routes under five different scenarios of demand, capacity, distance, and area were evaluated. The economic sustainability was assessed in terms of four supply chain costs (i.e., establishment, transportation, production and holding costs). Environmental sustainability was measured using the ReCipe method
  Keywords: environmental sustainability; green supply chain; multi-commodity; multi-objective optimisation; particle swarm optimisation; supply chain network design problem.
  DOI: 10.1504/IJISE.2022.10050355
   
  
• Lean Manufacturing Implementation in the Food Industry in Jordan  
  by Lubna Baqlah, Hala Alsliti, Mohammed Obeidat, Samir Khrais 
  Abstract: Lean manufacturing philosophy aim to enhance operation efficiency by eliminating wastes, which are considered non-value added activities that increase costs and reduce profits in the competitive marketplace. In this study, the lean manufacturing concepts were used using the value stream mapping, to highlight areas of improvement and eliminate wastes in a thyme manufacturing line in a food factory in Jordan. The data were collected using motion and time study concepts from the factory, and both the current and future state value stream maps were constructed. The results showed that when joining packing and labelling operations in the thyme manufacturing line, the lead time was successfully reduced by 13.57%.
  Keywords: lean manufacturing; value stream mapping; VSM; thyme; food industry; Jordan.
  DOI: 10.1504/IJISE.2022.10050436
   
  
• A framework for optimal patch release time using G-DEMATEL and Multi-Attribute Utility Theory  
  by Misbah Anjum, Amir H.S. Garmabaki, P.K. Kapur, Sunil Kumar Khatri, Vernika Agarwal 
  Abstract: The primary focus of the present work is to determine the optimal vulnerability patch release time using multi-attribute utility theory (MAUT) by considering two objectives that are cost minimisation and reliability maximisation. The novelty of the study lies in multi-phased research methodology for identifying the attributes affecting the software patch release time through a combination of literature review and the grey-Delphi approach for guiding the optimisation process. The literature has directly considered the weights of the attributes without emphasising their interrelationships, which is overcome by the use of the DEMATEL methodology under the grey environment in the current study for the evaluation of weights of selected attributes. The implications of the study will help in achieving the sustainable development goals pertaining to Innovation and Infrastructure. A numerical example is used to demonstrate the relevance of the optimisation problem.
  Keywords: vulnerabilities; patch release; multi-attribute utility theory; MAUT; reliability; cost; sustainable development goals; SDGs.
  DOI: 10.1504/IJISE.2022.10050488
   
  
• A Revenue-based Decision-making Approach for Evaluating Modular Product Release Plans under Resource Constraints  
  by Adewole Adegbola, Venkat Allada 
  Abstract: This study introduces the module substitution concept to develop an approach for assessing different strategies for modular product release in a technology-receptive market. We consider a situation where product variants emerge from modules which have varying modular relationships, and each module is defined by specific attributes. The statistical program evaluation and review technique (statistical PERT) was then adopted to address the uncertainties associated with module attributes. A practical example involving the development of modules, product families and their product variants is used to demonstrate the applicability of the approach in which feasible strategies that satisfy the development resource constraint were identified. We then introduced a substitute module to yield a product variant and re-evaluated the strategies. The results obtained shows that the approach is instrumental in assessing various alternatives based on launch timings and revenue generation and can be adopted by managers in deciding on the appropriate product release plan.
  Keywords: product release; product variants; resource constraint; module substitution; statistical PERT.
  DOI: 10.1504/IJISE.2022.10050936
   
  
• Ergonomics intervention with DMAIC methodology application  
  by Nur Nadia Nadirah Yusuf, Shaliza Azreen Mustafa, Rosmaini Ahmad 
  Abstract: This study aims to assess the level of ergonomics risk factors (ERFs) in production workstations using ergonomics assessment tools and provide an appropriate solution to improve the safety and health of the workers. A systematic approach using define-measure-analyse-improve-control (DMAIC) methodology was applied. Initial assessment found that awkward posture was the main ERF emerging in the company. Under the Measure and Analyse phases, the rapid upper limb assessment (RULA) and rapid entire body assessment (REBA) tools were applied to further assess of the identified ERFs. Results found that the filling task is the highest risk condition. Improve phase involved improvements action based on simple invention using a wooden step stool to provide a neutral working posture and the REBA has showed the signs of low risk comparatively. Related recommendations based on hazard identification, risk assessment and risk control (HIRARC) were then given in Control phase for future work planning.
  Keywords: ergonomics assessment; musculoskeletal disorders; MSD; DMAIC; RULA; REBA; food industry.
  DOI: 10.1504/IJISE.2022.10050938
   
  
• Implementation of Internet of Things (IoT) in Micro, Small and Medium Enterprises: A Case Study  
  by Parikshit Sarulkar, Kumar Srinivasan, Anish Kumar, Vineet Kumar Yadav 
  Abstract: Micro, small, and medium enterprises (MSMEs) play a vital role in India’s economic growth. MSMEs operating in the scrap management sector encounter two main concerns about the transportation cost and scheduling of vehicles. To solve these issues, MSMEs are trying to adopt emerging technologies such as automatic scrap storage, inventory control, and retrieval systems. However, MSMEs are reluctant to implement these technologies due to their pre-assumption of high adoption costs and expected benefits. The present study focused on the effective IoT implementation in vehicle loading to reduce transportation costs and trips in MSMEs. The case study of the scrap managing company has been considered to show the benefits of IoT implementation in MSMEs. The simulation was performed using FlexSim, and the results have confirmed that the IoT implementation can improve vehicle loading by 38% and reduce transportation costs by 38.6%. The outcomes highlight the benefits of IoT deployment in MSMEs.
  Keywords: micro; small; and medium enterprises; MSMEs; scrap management; IoT implementation; transportation; FlexSim simulation.
  DOI: 10.1504/IJISE.2022.10051062
   
  
• Dynamic Futures Margin Setting Method under State Dependence  
  by Wang Hong, Kun Wen, Shouqian Kang 
  Abstract: Margin is not only a basic risk control system for futures trading, but also an important part of the cost of futures trading, and its fundamental position is very important. This paper presents a dynamic margin setting method for futures based on market state, which considers extreme risk control and opportunity cost. In different market conditions, we choose different margin levels to better balance spillover probability and opportunity cost. Using machine learning, we sample the sugar futures traded on the Dalian Commodity Exchange between January 6, 2006, and May 29, 2020. The market is divided into three categories by the hidden Markov model: highly volatile, volatile, and stable. We compare margin level under VaR, CVaR, MMVaR, EWMA and improved EWMA risk standards. Comparative analysis and retrospective test show that the current fixed margin ratio is unreasonable, and the margin level under the single risk criterion cannot balance risk control and opportunity cost well. We recommend that market regulators dynamically adjust margin setting levels according to different market states, thereby luring more investors to invest and boosting the liquidity of the futures market.
  Keywords: dynamic margin level; risk criteria; machine learning; market status.
  DOI: 10.1504/IJISE.2022.10051100
   
  
• Modified Ant Colony Algorithm for Job Shop Scheduling Problem  
  by Ye Li, Ning Wang, Kun Xu 
  Abstract: In this work, we proposed a modified ant colony algorithm (ACA) for job shop scheduling problem (JSSP) with make-span, and constraints such as machine selection, time lags, and holding times, process, and sequence are taken into account. The two-stage setup of the pheromone update mechanism allows for a combination of local and global pheromone updates. In the first stage, the pheromone is updated locally for each completed process, and after the set iteration conditions have been met, the second stage is entered. To overcome the initial reliance on pheromones in the ACA, the pheromones are initialised using a genetic algorithm (GA). The optimal convergence ratio is obtained through the design of a genetic operator based on the procedure principle to accelerate the convergence effect of the whole algorithm and improve the global searching ability of ACA. Taking an engine company as an example, several simulation experiments are carried out for GA, ACA, and modified ant colony algorithm (MACA) based on the standard dataset to verify the effectiveness of proposed algorithms.
  Keywords: job shop scheduling problem; JSSP; ant colony algorithm; ACA; genetic algorithm; modified ant colony algorithm; MACA; optimal convergence ratio.
  DOI: 10.1504/IJISE.2022.10051301
   
  
• Efficient Bayesian optimization of bounded general loss function for robust parameter design  
  by YING CHEN, Mei Han 
  Abstract: Robust parameter design (RPD) has been generally employed to minimise the system quality loss caused by noise perturbation via setting control factors in engineering design. Bayesian optimisation algorithms have received increasing attention for RPD, which includes establishing the Kriging model and developing acquisition functions (AFs). In RPD, the quality loss function method is a common method to calculate the response deviation from a target value. The existing literature mainly focuses on setting the loss function as a quadratic function for easier calculation, while it is not always reasonable due to its unboundedness. In this paper, we propose three efficient Bayesian algorithms for bounded general loss functions for finding the optimal design of control factors based on a Kriging model. We develop a Monte Carlo sampling method to approximate the proposed AFs. Three numerical examples and a rocket injector case are used to demonstrate the effectiveness of the proposed algorithms.
  Keywords: Bayesian optimisation; robust parameter design; RPD; bounded general loss function; acquisition function; Gaussian process model.
  DOI: 10.1504/IJISE.2022.10051366
   
  
• Development of a Prescription Framework for Supply Chain Risk Management: Cases of Asian MNCs  
  by Jae-Yong Yang, Geun-wan Park, Kwangtae Park, Rajesh Piplani 
  Abstract: We use the level of impact and duration of risk to classify types of supply chain risks and their effective prescriptions using case studies of multi-national companies. The classification of supply chain risks and countermeasures for each risk type are presented as a risk diagnosis and prescription matrix. The companies adopt a risk acceptance strategy when the impact is low and the duration short. When impact is high and the duration short, substitute raw materials (or production sites) are considered under risk avoidance strategy. New suppliers and technologies are developed for complete replacement for risk mitigation when the duration of risk is long but the impact low. For risk-sharing, new demand sources are developed, and diversification of suppliers and production sites pursued when the risk duration is long and the impact high. Novelty of our study is in considering risk duration as an additional variable in risk management strategy.
  Keywords: supply chain risk; prescription matrix; Asian MNC.
  DOI: 10.1504/IJISE.2022.10051409
   
  
• A fast encryption method of large enterprise financial data based on adversarial neural network  
  by Youwei Chu  
  Abstract: In order to overcome the high time cost of encrypting, decrypting and revocation attribute calculation existing in traditional encryption methods of financial data of large enterprise, this paper proposes a fast encryption method of financial data of large enterprise based on adversarial neural network. Adversarial neural network is used to build the financial data reorganisation model of large enterprise, and obtain the sparse and local characteristics of the reorganised financial data of large enterprise, so as to generate the encrypted initial key and sub-key, and complete the fast encryption of the financial data of large enterprise by combining matrix transformation. The simulation results show that the average time cost of encryption is 0.115 s, the average time cost of decryption is 0.05 s, and the average time cost of undo calculation is 0.616 s, which can realise the fast encryption of financial data of large enterprise.
  Keywords: adversarial neural network; data encryption; enterprise financial data.
  DOI: 10.1504/IJISE.2021.10052415
   
  
• An Enterprise Financial Data Risk Prediction Model Based on Entropy Weight Method  
  by Wenyuan Chen 
  Abstract: The traditional financial risk prediction model has some problems, such as inaccurate prediction results due to the poor selection of risk index system. This paper proposes to build an enterprise financial data risk prediction model based on entropy weight method. Build the enterprise risk financial data prediction index system and obtain the prediction index; The entropy weight method is used to calculate the weight of prediction index and obtain the weight coefficient; The data with higher risk index weight is input into the neural network as the initial vector of prediction, the weight of risk data nodes at different levels of the network is calculated, the risk prediction model is constructed, and the error of the output solution of the model is corrected by the incentive function to realise the risk prediction. The experimental results show that the prediction accuracy of the model is always about 98%.
  Keywords: entropy method; enterprise financial risk; index system; weight; predictive model.
  DOI: 10.1504/IJISE.2021.10052416
   
  
• Judgment Method of Enterprise Financial Data Abnormality Based on High-Order Dynamic Bayesian Network  
  by Lili Wang  
  Abstract: This paper proposes a judgement method of enterprise financial data anomaly based on high-order dynamic Bayesian network. Firstly, the enterprise financial data is divided into normal data and abnormal data, and the original training samples are classified to obtain the data classification results. Input the classification results into the enterprise financial data management platform based on cloud computing to improve the efficiency of data anomaly judgement. The high-order dynamic Bayesian network is used to initialise and modify the network, and the chromosome coding method is used to realise the abnormal judgement of enterprise financial data. The experimental results show that the method has higher accuracy rate of anomaly judgement, lower miss rate and error rate.
  Keywords: high-order dynamic Bayesian network; financial data; network modification; chromosome coding; data classification.
  
  
• Study on regional digital teaching resource sharing platform based on Internet of things and big data  
  by Xiaohong Zhu 
  Abstract: In order to overcome the problems of low upload rate and poor data integrity of traditional teaching resource sharing platforms, the paper proposes a regional digital teaching resource sharing platform based on the internet of things and big data. Introduce the least square algorithm to construct the operation and maintenance elastic model, and calculate the dual residual and the original residual of the model output data. The platform adopts the WebAPI framework, including the design of user login service, teacher resource information service, teaching information service, and online recommendation service for sharing teaching information. The experimental results show that the platform designed in this paper has a higher transmission rate, which has been maintained above 4G/s with the increase of time. In the state of network interruption, the platform’s return matrix data status detection shows that the storage data of the platform in this paper does not appear abnormal.
  Keywords: internet of things; big data; ADMM algorithm; operation and maintenance elasticity; dual function; lagrange function.
  
  
• Abnormal Recognition of Corporate Financial Data Based on Deep Belief Network  
  by Xi Lun, Xiangyang Zhang, Yining Wang, Tian Wang 
  Abstract: In view of the traditional enterprise financial data exception recognition methods such as low recognising precision, long, belief network is put forward based on the depth of the enterprise’s financial data anomaly identification method, adopts the distributed data collection method, selection of enterprise financial data mining, and correlation analysis, according to the financial data of sample information entropy, to divide the financial data flow, According to the extraction results, use the deep belief network to build a financial data anomaly recognition model. The financial data of enterprises are input into the abnormal identification model of financial data to identify the status of financial data. Experimental results show that this method has higher recognition accuracy and shorter recognition time.
  Keywords: deep belief network; corporate financial data; information entropy; data stream fragment.
  DOI: 10.1504/IJISE.2021.10052450
   
  
• A clustering method of marketing effective data based on relation matrix fusion  
  by LinLin Zhou  
  Abstract: In order to overcome the problems of traditional clustering methods, such as low recall rate, low clustering accuracy and poor clustering efficiency, an effective marketing data clustering method based on relation matrix fusion is proposed. The relationship matrix fusion process is designed, and the effective data in the marketing data is selected according to the fusion results. Then, the feature units of effective marketing data are extracted, and the data clustering problem is transformed into a linear programming problem by calculating the EMD distance between the data. Finally, data clustering is completed according to the results of data integration. The experimental results show that the recall rate of effective marketing data is between 94.5% and 98.3%, the clustering accuracy is between 95.1% and 98.7%, and the maximum number of iterations is 900, which proves that the method achieves the design expectation.
  Keywords: marketing data; valid data; relation matrix fusion; EMD distance; data clustering; Earth mover’s distance.
  DOI: 10.1504/IJISE.2021.10052451
   
  
• Integration of Modified FMEA Approach with Industry 4.0 Technologies to Improve Reliability of Lean Systems  
  by Karthik Subburaman, Balaji Kuppusamy 
  Abstract: Many organisations are using lean tools with Industry 4.0 technology these days to enhance the sustainability of their lean manufacturing systems. This article contributes by combining a lean tool (modified FMEA) with Industry 4.0 technologies to improve the dependability of lean systems. Under the four lean subsystems of personnel, equipment, materials, and schedules, a redesigned FMEA framework incorporating Industry 4.0 technologies is proposed. From the mapping of lean wastes, Inventory accounts for 39% of total waste, overproduction accounts for 23% of total waste, defects account for 22% of total waste, non-utilised talent accounts for 16% of total waste based on RAV calculation from modified FMEA table. Also based on RPN calculation from the modified FMEA table, Inventory accounts for 45% of total waste, overproduction accounts for 23%, of total waste, defects account for 19% of total waste, non-utilised talent accounts for 13% of total waste.
  Keywords: lean; Industry 4.0; personnel; equipment; materials; schedules.
  DOI: 10.1504/IJISE.2022.10052748
   
  
• In-house part supply logistics optimisation based on the workforce’s ergonomic strain and environmental considerations  
  by Parames Chutima, Chayanee Prakong 
  Abstract: This paper focused on in-house part supply logistics adopted by an automotive manufacturer to make just-in-time deliveries of parts from a supermarket to mixed-model serpentine-shaped assembly lines without shortage. Five objectives are optimised simultaneously, i.e., minimising the total number of tours, minimising the number of tow train drivers, minimising the energy expenditure load discrepancy among tow train drivers, minimising the total inventory kept at the border of the line and minimising the total PM2.5 emission released by a fleet of tow trains. The mathematical model is formulated for the problem. Due to its NP-hard in nature, multi-objective metaheuristics have to be developed for solving practical-sized problem instances. As a result, the non-dominated sorting teaching-learning-based optimisation III (NSTLBO III) which is a hybrid of the non-dominated sorting genetic algorithm III (NSGA III) and teaching-learning-based optimisation (TLBO) is proposed to solve the problem. The results show that NSTLBO III outperforms NSGA III and the multi-objective evolutionary algorithm based on decomposition (MOEA/D) in terms of qualitative, convergence-related and comprehensive metrics.
  Keywords: part feeding; automotive industry; multi-objective optimisation; NSGA III; TLBO.
  DOI: 10.1504/IJISE.2023.10053335
   
  
• Contract Design and Order Decision of Online Retailer's Sharing Surplus Demand with Offline Retailer  
  by Jianjun Yu, Liqian Wang, Yongwu Zhou, Hongkai Fang 
  Abstract: With the gradual slowdown in the growth rate of e-commerce transaction volume, some online retailers choose to cooperate with offline retailers to jointly explore a profit increment path and achieve a new round of profits. In this cooperation mode, the online retailer will share his surplus demand with the offline retailer to complete. This paper designs two kinds of contracts to distribute the cooperative income. In contract 1, the online retailer will return a certain proportion of the cooperative profit to the offline retailer. While in contract 2, the online retailer will provide certain subsidies to the offline retailer according to his order quantity. The results show that contract 1 is dominant when the offline retailer is faced with high demand or slow-volatile demand. However, contract 2 outperforms in the contrary situation. Through sensitivity analysis, it is found that changing the cost or price of the offline retailer can improve the performance of both retailers.
  Keywords: random demand; Stackelberg game; transshipment; revenue sharing contract; ordering decision.
  DOI: 10.1504/IJISE.2022.10053400
   
  
• Integrating noncyclical preventive maintenance scheduling and production planning for a series-parallel production line with stochastic dependence  
  by Ziyad Bahou, Krimi Issam, Abdessamad AitElCadi, Nizar Elhachemi 
  Abstract: This paper investigates the integrated non-cyclical preventive maintenance scheduling and production planning for a series-parallel production line. We consider the stochastic dependence between the components of each subsystem. This problem has not been studied so far in the literature even though it represents a realistic configuration. First, we compute the available production capacity restricted by the stochastic dependence. Then, an integer linear program is used to determine the optimal production plan and preventive maintenance schedule. The results show that ignoring the stochastic dependence effect causes many unexpected consequences and additional production and maintenance costs. This work provides practitioners with a set of managerial insights to develop adequate integrated production and maintenance policies.
  Keywords: production planning; maintenance scheduling; integer programming; series-parallel production; stochastic dependence.
  DOI: 10.1504/IJISE.2022.10053547
   
  
• Ergonomic assessment for work-related musculoskeletal disorders: A case study on office workers in two government organisations in the United Arab Emirates  
  by In-Ju Kim 
  Abstract: This study investigated the pervasiveness of work-related musculoskeletal disorders (WMSDs) amongst office workers from two government organisations (A and B) in the United Arab Emirates. The primary data were collected by self-administrative questionnaire (SAQ), Nordic musculoskeletal survey, and ergonomic assessments with the Rapid Office Strain Assessment (ROSA) checklists. The SAQ survey from organisation A showed that the respondents’ most common pain was the neck (80.00%), whilst organisation B was lower back (78.57%). According to the ROSA results, 86.49% of the respondents in organisation B required ergonomic investigations, whilst 61.76% in organisation A worked under the risk warning regions.
  Keywords: ergonomic assessment; musculoskeletal disorders; MSDs; Rapid Office Strain Assessment; ROSA; work-related musculoskeletal disorders; WMSD; office workers; United Arab Emirates; UAE.
  DOI: 10.1504/IJISE.2022.10053618
   
  
• Product to Process: An Ontology-based approach for product manufacturing process in Flexible Manufacturing System  
  by Imane ZAHRI, Mohamed RHAZZAF, Souhail SEKKAT, Mohammed DOUIMI 
  Abstract: Given the importance and gain of acquiring flexibility and interoperability, the reconfigurability of manufacturing systems remains an active subject of industry research. We propose, in this paper, an ontology-based model for the product’s process for a flexible manufacturing system. This model avoids the technical difficulties related to the product manufacturing design and offers a conversion of the product manufacturing process semantic description to its technical implementation inside the manufacturing system. The ontology is based primarily on the components of the production system and the product life cycle process. We have tested our approach in a flexible cell case study to have the new product manufacturing process using a depth first search-based algorithm applied to the proposed ontology.
  Keywords: product lifecycle management; PLM; new product development; NPD; reconfigurability; ontology; depth first search; DFS; Semantic Web.
  DOI: 10.1504/IJISE.2023.10053656
   
  
• Optimization of reactive precipitation for processing reject brine in Ammonium Perchlorate manufacture  
  by RAMACHANDRA RAO, TIDE P. S, Benny.K George, AJITH PRASAD, JOJO MATHEW 
  Abstract: Ammonium perchlorate (AP) is the most widely used oxidiser for solid rocket propellant formulations. AP manufacture generates a reject brine with 7-8% perchlorates by weight. An optimisation study of reactive precipitation for transforming reject brine into an admixture of AP with ammonium chloride and sodium bicarbonate was conducted at 0.3 m3/batch capacity. The operating conditions play a crucial role in the kinetics of reactive precipitation. Taguchi design and statistical ANOVA were applied to estimate significant operating conditions and their contributions to process performances. The study reveals the potential impact of mixing, type of impeller configuration, and temperature on the process efficiency. A regression model was developed to predict process performances and Taguchi optimum conditions used for getting desirable performances. Subsequently, a multi-response optimiser was applied to find out a set of operating conditions for improving process performance. Experimental validation trials and characterisation were conducted with modified operating parameters and results show considerable improvement in process efficiency.
  Keywords: Taguchi design; ammonium perchlorate; sodium bicarbonate; reactive precipitation; reject brine; optimisation; mixing; regression analysis.
  DOI: 10.1504/IJISE.2022.10053681
   
  
• Experimental modeling and multiobjective optimization of electrochemical discharge peripheral surface grinding process during machining of alumina epoxy nanocomposites  
  by Nandani Singh, Vinod Yadava, Pragya Shandilya 
  Abstract: Machining electrically non-conductive materials is still a very challenging task. So far, electrochemical discharge machining (ECDM) and its configurations, such as drilling-ECDM, TW-ECDM, and milling-ECDM, have been developed for machining such materials. Hence, an in-depth experimental analysis of grinding-ECDM is also required. In the present work, the mathematical models have been formulated using response surface methodology based on Box-Behnken design on the peripheral surface configuration of grinding-ECDM (electrochemical discharge peripheral surface grinding process). Experiments were carried out on alumina-reinforcement epoxy nanocomposites considering supply voltage, pulse on-time, electrolyte concentration, and wheel rotation as input process parameters and MRR and Ra as output performance parameters. The multi-objective optimisation has been done using desirability function analysis (DFA) and grey relational analysis (GRA). The input process parametric conditions obtained from both optimisation methods are different. It has been found that DFA shows slightly better results than GRA for both MRR and Ra.
  Keywords: desirability function analysis; DFA; ECDM; ECDPSG; Grey relational analysis; GRA; grinding; multi-objective optimisation; polymer nanocomposite; PSN.
  DOI: 10.1504/IJISE.2023.10053851
   
  
• An empirical investigation of Lean Manufacturing dimensions through Structural equation modeling  
  by Amjad Khalili 
  Abstract: This paper aims to recognize Lean Manufacturing (LM) two dimensions namely soft (SLM) and hard lean (HLM). It empirically examine the linkages between these perspectives and contributes to operations management literature by focusing on the aspects managers attempt to embrace in their industries. It censoriously examines these practices as scarce research discussed these together. Besides, the importance of their adoption is reflected and relevant key factors introduced. To achieve this, a conceptual model is established and its applicability explored. The postulated hypothesis is further tested through data gathered from Palestinian industries through the developed questionnaire and both SPSS 23 and AMOS 23 are considered for analysis. Findings imply that both can be found together and their linkages is supported. Manufacturers may apply these models to establish a better implementation environment in their manufacturing facilities using the appropriate integrated LM frameworks.
  Keywords: Soft lean; hard lean; importance; model; applicability; AMOS.
  DOI: 10.1504/IJISE.2023.10054049
   
  
• Advanced delay-time analysis applied to carbon black powder production  
  by Marc Fischer, Bryan Jones 
  Abstract: Delay time models (DTM) for series systems divide the failure of a system into the appearance of defects and the delay until the breakdown. During this work, we developed four new DTM whose main novelty consists of considering cheaper online inspections preceding offline inspections. Our numerically tested models were applied to a previously published study about a carbon black factory. After the improvement of problematic assumptions, the optimal inspection period turns out to be considerably larger than in the previous study, which emphasises the need to flexibly develop new delay-time models when facing unusual situations and to avoid a reliance on black boxes. The correct handling of environmental consequences has a tremendous impact upon the optimal maintenance decision.
  Keywords: Delay Time; Poisson Process; Maintenance; Monte-Carlo.
  DOI: 10.1504/IJISE.2023.10054146
   
  
• Elaboration of Water Distribution Schedules in Periods of Scarcity  
  by Tatiana Balbi Fraga, Aldênia Karla Barrêto Candido, Marcos Henrique, Abdeladhim Tahimi 
  Abstract: Scarcity of treated water is a global problem that directly impacts the quality of life and, therefore, brings to light the need for more careful management of water resources in locations that are affected. Although there is a vast literature on the problem of design and operation of treated water distribution networks, few studies consider the issue of scarcity. In the present paper, we carry out a study on the real water distribution problem of a city located in Northeastern Brazil. In this study, we conceptually and mathematically model the problem addressed as a new specific water distribution optimisation problem, and implement the model developed using LINGO software from LINDO systems. We conclude the study showing that the developed solver is a practical, effective and efficient tool, which can be easily used by a suitably qualified employee.
  Keywords: mathematical modelling; water distribution schedules; water distribution system; WDS; water scarcity; LINGO.
  DOI: 10.1504/IJISE.2023.10054305
   
  
• QUALITY ASSURANCE USING ACCELERATED LIFE TESTING VIA REBATE WARRANTY  
  by Showkat Ahmad Lone, Intekhab Alam, Sabir Ali Siddiqui, Ritu Rathee 
  Abstract: Accelerated life testing (ALT) has now become the primary method for rapidly assessing product reliability. Designing highly effective test models is a vital step in ensuring that ALT can properly, quickly, and economically assess product reliability. These tests subject the sample to high levels of stress. Then, based on the stress-life relationship, the failure data can be extrapolated from a sample at a high-stress level to a normal level to calculate product life at usual operating conditions. The study is an advanced proposal to analyse ALT schemes for the quality improvement and reliability of modern products. The problem is investigated using constant stress, assuming that the unit lifetimes follow the power-function distribution. Furthermore, as a procedure that employs ALT to predict the cost of age replacement of goods covered by a warranty agreement. A mathematical example is also used to demonstrate theoretical findings. The result will prove an asset for marketing providers in estimating the various costs associated with the product under the warranty policy. Hence, it can help manufacturers to increase the reliability/quality of their products to achieve consumer satisfaction.
  Keywords: product life acceleration; age-replacement warranty; power-function model; simulation analysis; accelerated life testing; ALT.
  DOI: 10.1504/IJISE.2023.10054347
   
  
• Just in Time and Supply Chain Finance: A Hierarchal Model Development  
  by Imran Zaman, Md. Ramjan Ali, Sharfuddin Ahmed Khan 
  Abstract: This study aims to investigate whether or not just in time (JIT) and supply chain finance (SCF) have a synergistic impact on overall organization performance. The purpose of this study is to help address this knowledge gap and shed light on the role that JIT tools plays in optimizing the benefits that SCF offers to an organization as a whole. Interpretive structural modeling (ISM) and Decision making trial and evaluation laboratory (DEMATEL) has been used to explore relationship, contextual link and hierarchical interpretation between JIT and SCF characteristics. Results shows that, the variables with the highest rankings in ISM and DEMATEL are employee attitude, embeddedness of continuity practice, and changing level and position of inventory maintained by businesses. Integration of JIT and SCF will help organizations to increase in revenues, a reduction in waste, an improvement in cash flow, and a reduction in supply chain hazards.
  Keywords: Supply chain finance; Just-in-time; Organizational performance; Interpretive Structural Modeling; Decision Making Trail and Evaluation Laboratory; FMCG industry.
  DOI: 10.1504/IJISE.2023.10054392
   
  
• Profit Analysis of Utensils Manufacturing System of Steel Industry  
  by Sapna Saini, Jitender Kumar, M.S. Kadyan 
  Abstract: The objective of the present study is to deal with the profit analysis of utensils manufacturing system of steel industry which has six subsystems:
  Keywords: profit analysis; steel industry; availability analysis; supplementary variable technique; utensils manufacturing system.
  DOI: 10.1504/IJISE.2022.10054606
   
  
• Value chain analysis of Biodiesel production from animal fat: A case of Botswana  
  by Nosi K. P. Moakofi, Jerekias Gandure, Venkata P. Kommula 
  Abstract: Botswana as a developing country, is currently investing in biodiesel production, however, no data on biodiesel value chain characterisation is available to establish viability and sustainability of biodiesel production. This study characterised the value chain of animal fat biodiesel in respect of feedstock supply, production, and end use. The purpose of the study was to assess potential of animal fat feedstock to sustain envisaged biodiesel industry in Botswana. Methods used in the study include questionnaire surveys and interviews. Key findings of the study indicate that animal fat-based biodiesel value chain is unstructured, stakeholders are disintegrated and unregulated, and the country produces enough fat to yield tallow potential to produce 205,345 litres of biodiesel per month. The findings indicate the need for regulating and promoting animal fat-based biodiesel value chain with policies and establishment of entities to integrate value chain stakeholders as well as exploring all opportunities within the value chain.
  Keywords: value chain; biodiesel; animal fat; production; Botswana.
  DOI: 10.1504/IJISE.2023.10054811
   
  
• Reliability Assessment of Dragline’s subsystem using Dynamic Bayesian Network  
  by Deepak Kumar, Debasis Jana, Suprakash Gupta, Pawan Kumar Yadav 
  Abstract: Draglines are very complex in design and consist of hundreds of components. Ensuring the high reliability of a dragline is essential for the economic sustainability of a surface mining project. This study proposes a methodology for the reliability assessment of the dragline’s subsystem using the dynamic Bayesian network (DBN). The reliability of the dragging subsystem highly depends on the reliability of the drag brake, drag socket, and power failure. The dragging subsystem reliability is 84.29% at 1 hr. of machine operation. This study provides useful data for dragline maintenance planning and a reliability design.
  Keywords: dynamic Bayesian network; DBN; reliability; dragline; opencast mine; mining machine.
  DOI: 10.1504/IJISE.2023.10054814
   
  
• 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