International Journal of Applied Decision Sciences (29 papers in press)

Regular Issues

• Optimising online order batching operations in a manual order picking warehouse: a genetic algorithm approach  
  by Md. Saiful Islam, Md. Kutub Uddin 
  Abstract: This study is significant in the field of warehouse management and logistics. In manual order picking, online order batching requires balancing between order processing costs and customer expectations. The objective of this research is to address the challenges associated with online order batching, batch sequencing, and picker routing in a consistent manner, with the goal of minimizing both order processing and tardiness expenses of customer orders that arrive dynamically over time. This paper suggested a solution approach for the associated problems and combined a genetic algorithm (GA) with the variable time window batching method. The experimental findings indicate that the GA-based approach yields promising results within a reasonable computational timeframe. This significant development has the potential to transform traditional warehouse management procedures by making them more adaptable, flexible, and capable of addressing the dynamic demands of modern business environments.
  Keywords: online order batching; order picking; variable time window batching; genetic algorithm; greedy routing.
  DOI: 10.1504/IJADS.2025.10066980
   
  
• Modeling and computational simulation of operations and queue systems in a supermarket  
  by Paloma Dos Santos Alves Nunes, João Vitor Da Silva Alves, Yuri Laio Teixeira Veras Silva 
  Abstract: Queuing theory, combined with computational simulation, has been employed to analyse queue system configurations with the objective of identifying scenarios that offer the greatest benefits to the company. This study aims to develop a computational simulation approach to determine the optimal number of cashier operators in a supermarket in Brazil, considering queue-related parameters at different times of the day and month to achieve efficient wait time management. The results indicate that the best number of fast queue attendants (AQF) is two operators across all configurations. For normal queue attendants (AQN), at the beginning of the month, 15 attendants are needed on weekdays and 14 on weekends. Mid-month, the demand decreases, with 13 attendants required on weekdays and 14 on weekends. At the end of the month, the optimal allocation is 15 attendants for both weekdays and weekends.
  Keywords: queues modelling; computational simulation; discrete-event modelling; agent-based simulation; AnyLogic; supermarket operations.
  DOI: 10.1504/IJADS.2025.10067360
   
  
• Optimisation of urban distribution paths for electric logistics vehicles based on shared pre-positioning warehouse mode  
  by Yanfeng Wu, Xueping Liu, Kai Tian 
  Abstract: To address the problems of high cost, low efficiency and load rate of urban logistics transportation, this work studies the electric vehicle routing problem. First, a novel urban logistics distribution mode, namely, the shared pre-positioning warehouse mode (SPWM) is proposed, including pick-up stage and distribution stage. Second, considering the constraints of time window, vehicle capacity, and battery capacity, the electric vehicle route optimisation models are established for the two stages of the SPWM. Third, an improved genetic algorithm by combining elite retention strategy and swap operator is proposed to solve the model. Finally, we evaluated the proposed mode on a real-world case including five suppliers and thirty customers. Compared with the path optimisation results of the traditional distribution mode (TDM), the SPWM reduces the total cost by 14.85%, the travelled mileage by 66.15%, and improved the load rate by 124.09%, which verify effectiveness of the proposed model and algorithm.
  Keywords: urban logistics distribution; shared pre-positioning warehouse; electric vehicle routing problem; genetic algorithm.
  DOI: 10.1504/IJADS.2025.10067556
   
  
• Digital entrepreneurship: the influence of social media on consumer buying behaviours  
  by António José Mendes Ferreira, Paulo Jorge De Almeida Pereira, Inês Gouveia Da Costa, Dagoberto Ivo Sousa Couto Dos Santos 
  Abstract: The aim of this study is to understand the extent to which consumers are willing to follow certain brands and companies, seeking to understand which content appeals to them the most, how they interact on new digital platforms, their frequency of purchases, and the impact that brands have on their purchasing decisions. In order to assess the effect that social media has on brand relationships, it is important to investigate the new entrepreneurial trend, namely, digital entrepreneurship, as this phenomenon is on the rise both in terms of business digitalisation and the creation of digital companies. For this purpose, this study adopted a quantitative methodology, based on a questionnaire survey, which gathered 199 responses. It was concluded that the majority of individuals feel influenced by brand and company posts. They consider this to be one of the reasons that lead them to purchase goods and services through these platforms, constituting an important contribution to business practice. Regarding the aspects that vary this impact, it was concluded that this variation is only significant in certain cases.
  Keywords: digital entrepreneurship; social media; online consumption.
  DOI: 10.1504/IJADS.2025.10067952
   
  
• Minimising makespan and total tardiness in no-wait open-shop scheduling problems using metaheuristic algorithms: a narrative review  
  by Mirpouya Mirmozaffari  
  Abstract: This study begins with a comprehensive narrative review of shop layout and task scheduling, establishing its novelty by being the first to apply an open-shop nonlinear methodology to four metaheuristic algorithms. The research addresses the no-wait open-shop scheduling problem (NWOSP) through a mixed integer nonlinear problem (MINLP) framework, focusing on minimising completion time (Makespan) and total tardiness while considering machine availability, job sequencing, and machine-to-machine transfer durations influenced by job types. An innovative transportation method with unlimited capacity eliminates delays. Comparative analysis reveals that particle swarm optimisation (PSO) and simulated annealing (SA) consistently outperform other algorithms, while Harris Hawks optimiser (HHO) and genetic algorithm (GA) show competitive performance in specific cases. The study integrates bi-objectives using reference point programming with Euclidean distances (RPPED) into a single nonlinear objective, contributing to operations research (OR) with streamlined optimisation processes, enhancing practical applications for complex scheduling problems.
  Keywords: particle swarm optimisation; PSO; Harris Hawks optimiser; HHO; genetic algorithm; simulated annealing; narrative review; open shop scheduling; mixed integer nonlinear programming; metaheuristic algorithms; makespan; total tardiness; transportation time.
  DOI: 10.1504/IJADS.2025.10068509
   
  
• A strategic choice approach to higher education curriculum design  
  by Rafael Verão Françozo 
  Abstract: The Higher Education Curriculum (HEC) requires periodic reviews for reasons such as curriculum updates or compliance with legal requirements. This revision can be a complex task, involving multiple authors with varying perspectives and producing effects for several years after the next revision. The objective of this study is to describe the process of restructuring the curriculum of a higher education course in Information and Communication Technology. The restructuring was carried out in an environment where there were disagreements among the actors involved regarding the decisions required in the process. The Strategic Choice Approach Problem Structuring Method was adopted as a means of generating consensus among those involved. The main results indicate that the methodology helped to improve decision-makers' learning in the process and to make consensual decisions, being essential in increasing the number of graduated students.
  Keywords: problem structuring methods; instructional design; group decision; stakeholder consensus; participatory decision-making; information and communication; technology; curricular restructuring; decision-making framework; educational planning.
  DOI: 10.1504/IJADS.2026.10069087
   
  
• Design of construction project management technology based on project schedule cost model and swarm intelligence algorithm  
  by Hua Tian 
  Abstract: To solve the problems of cost estimation and schedule control in project management, a project management technology design based on project schedule cost model and improved particle swarm algorithm is proposed. This study uses quantitative field surveys and structural equation modelling analysis methods, combined with empirical analysis of 20 repeated experiments, to verify the effectiveness of the proposed method. The results showed that the improved particle swarm algorithm improved accuracy by 30.48% compared to the standard particle swarm, shortened project cycles by 15.2%, and reduced resource waste rate by 31.4%. In addition, the risk response time has been accelerated by 50.0%, the engineering quality qualification rate has increased by 7.4%, the progress tracking error reduced to 60.0%, and the decision-making time shortened to 42.7%. The research model not only enriches the optimisation theory of project management, but also has practical value in improving the intelligence level of project management and enhancing cost control capabilities.
  Keywords: project management; cost estimation; progress control; particle swarm optimisation algorithm; intelligentisation; empirical analysis.
  DOI: 10.1504/IJADS.2026.10069112
   
  
• Credit scoring using neural networks and SURE posterior probability calibration  
  by Matthieu Garcin, Samuel Stéphan 
  Abstract: In this article we compare the performances of a logistic regression and a feed forward neural network for credit scoring purposes. Our results show that the logistic regression gives quite good results on the dataset and the neural network can improve a little the performance. We also consider different sets of features in order to assess their importance in terms of prediction accuracy. We found that temporal features can be an important source of information resulting in an increase in the overall model accuracy. Finally, we introduce a new technique for the calibration of predicted probabilities based on Stein's unbiased risk estimate (SURE). This calibration technique can be applied to very general calibration functions such as the sigmoid function and the Kumaraswamy function, which includes the identity as a particular case. We show that stacking the SURE calibration technique with the classical Platt method can improve the calibration results.
  Keywords: deep learning; credit scoring; calibration; Stein’s unbiased risk estimate; SURE.
  DOI: 10.1504/IJADS.2026.10069617
   
  
• An integrated multi-criteria decision-making and fuzzy cognitive maps for prioritising barriers to geothermal energy development  
  by Seyyed Ali Piri Azizlou, Mohammad Reza Maleki 
  Abstract: This research identifies the factors contributing to the success of the Sablan geothermal power plant. For this purpose, the criteria are screened using the WASPAS method and then entered into a fuzzy cognitive map (FCM) to estimate cause-and-effect relationships. In the quantitative part, the model's reliability is validated through a questionnaire and feedback from experts. Another questionnaire is then designed using indicators identified during the super-composition stage. The results indicate that "resource extraction costs" received the highest score among all examined criteria. The results also demonstrate that the proposed technique addresses the limitations of one-way cause-and-effect relationships in the Balanced Scorecard. Additionally, three types of relationships including upward, two-way and downward relationships are identified in the FCM. Finally, it is concluded that the strategic structure of the Sabalan power plant requires a daily commitment to excellence in operational processes and product development on limited platforms to ensure the plant's effectiveness.
  Keywords: geothermal energy; weighted aggregated sum product assessment; WASPAS method; fuzzy cognitive map; FCM; Sablan geothermal power plant.
  DOI: 10.1504/IJADS.2026.10070144
   
  
• Supply chain equilibrium optimisation considering moderate resilience and costs  
  by Lan Xu, Xinyi Yan 
  Abstract: Supply chain resilience (SCR) plays a significant role in coping with unexpected risks. In view of the problem that excessive improvement in resilience has undermined enterprise profits, this study constructs a three-level supply chain (SC) of supplier-manufacturer-demand market and pre-embedded resilience in the SC through three measures: multi-source procurement, safety inventory, and backup procurement. Combined with risk severity, SC disruption loss rate is introduced to adjust the equilibrium between moderate resilience and costs. To minimize SCR costs and SC disruption loss rate, and stabilize the ratio of resilience costs to SC disruption loss within an interval, we construct a multi-objective optimization model considering sudden risk events and solve it using the Nondominated Sorting Genetic Algorithm II (NSGA-II). The results indicate that adjusting the resilience improvement measures according to the SC losses caused by risks can reduce disruption losses while maintain low resilience costs.
  Keywords: supply chain resilience; SCR; moderate resilience; MR; resilience costs; RCs; supply chain equilibrium; SCE.
  DOI: 10.1504/IJADS.2026.10070453
   
  
• Estimating the situation of customers' claim for compensation in travel insurances using machine learning  
  by Yusufcan Yaldiz, Adnan Corum 
  Abstract: One of the primary concerns for insurance companies is when policyholders file claims after experiencing accidents or other covered events as specified in their policies. Once a claim is initiated, the settlement process usually requires a thorough review, which plays a critical role in influencing customer loyalty and satisfaction. Considering this, the current study seeks to streamline travel insurance claim processes through automation and improve decision-making capabilities. By employing Logistic Regression and Naive Bayes estimation models, using a limited number of variables and an imbalanced dataset, the study evaluates the probability of travel insurance claims. Additionally, Principal Component Analysis is used for reducing dimensions. All analyses are performed using Python and its associated libraries. Ultimately, the results indicate that the implemented models can function as effective decision support tools.
  Keywords: travel insurance; insurance claims logistic regression; naive Bayes; principal component analysis; machine learning.
  DOI: 10.1504/IJADS.2026.10070558
   
  
• Research on the relationship between logistics satisfaction and e-commerce satisfaction: EC quality as a control variable  
  by Jhong-Min Yang, Jing-Ru Qiu, Ming-Han Chiang, Yu-Hsien Wang 
  Abstract: This study aims to answer the question, "If e-commerce quality (ECQ) is controlled, can logistics satisfaction (LS) still be used to predict EC customers' overall satisfaction with the EC business?" With 326 valid samples through an online questionnaire survey, this study used AMOS statistical software for structural equation modeling analysis. The analysis results show that although ECQ is controlled as a control variable (CV), the customer satisfaction brought by pure logistics can still significantly affect customer satisfaction with EC performance. Moreover, logistics convenience (LC) and logistics quality (LQ) have significant predictive capabilities for LS. Our findings have important implications for managers and policymakers, advocating that EC companies adopt proactive business methods and policy formulation covering logistics operations.
  Keywords: logistics convenience; logistics quality; logistics satisfaction; e-commerce satisfaction;ECQ.
  DOI: 10.1504/IJADS.2026.10070633
   
  
• Wildfire detection and suppression using multiple UAVs based on improved ant colony algorithm  
  by Xiaoyan Zhao, Peng Geng, Fanglin Xue, Rui Chen, Huizhen Hao 
  Abstract: With global climate change, the severity and frequency of forest fires are increasing, posing a significant threat to ecosystems and human settlements. Fire spots are often dispersed across multiple locations, and factors such as the presence of combustible materials, prevailing climate and weather conditions, and topographical features contribute to the rapid spread of fires. Addressing the challenge of effectively extinguishing forest fires, this study proposes an advanced solution utilising unmanned aerial vehicles (UAVs) for both detection and suppression. The core of this solution is a novel multi-UAV coordination mechanism that leverages swarm intelligence to detect and suppress forest fires. Specifically, this paper proposes an improved ant colony algorithm that incorporates principles of competition and cooperation by introducing repulsive pheromones and attraction information, thereby optimising the strategy for UAVs searching for fire spots. Additionally, to better align with real-world scenarios and enhance the practical applicability of UAVs, this paper improves the wildfire spread model by incorporating dynamic parameters, and introduces a practical UAV flight model that includes advanced obstacle avoidance functionality and considers the relationship between flight speed and water load. Simulation experiments under different environments are conducted to evaluate the performance of UAVs in detecting and suppressing wildfire spread. The experimental results demonstrate that the proposed scheme exhibits greater effectiveness and robustness in extinguishing forest fires with a shorter convergence time and a broader coverage of the search area. These improvements collectively contribute a valuable tool for forest fire management and rapid response.
  Keywords: multiple unmanned aerial vehicles; ant colony algorithm; wildfire detection; fire suppression.
  DOI: 10.1504/IJADS.2026.10070655
   
  
• Research on strategy selection for innovation through multi-party cooperation between police and enterprises based on evolutionary game theory  
  by Qilei Wang 
  Abstract: Framed against the backdrop of technology bolstering policing efforts through cooperation, a three-party game model involving public security organ, research institute, and production enterprise is established. By identifying the equilibrium points of the model, a detailed analysis is conducted on how parameters like costs, benefits, and usage levels influence the system’s evolutionary path. Moreover, an examination of the contributions to cooperative innovation by the three parties is undertaken. The research findings indicate that an increase in expected additional revenue per unit product and sales volume, along with reduced total costs of cooperative innovation, will effectively steer public security organ, research institute, and production enterprise towards cooperative innovation. Optimal evolution towards cooperative innovation can be achieved through the implementation of well-thought-out strategies that streamline the cost and benefit distribution mechanism. Notably, substantial compensation mechanisms can incentivize all units to opt for cooperative ventures, ensuring the system evolves towards Pareto optimality. It is worth highlighting that as the end-users of products, public security organ play a pivotal role in the process.
  Keywords: police-enterprise cooperation; cooperative innovation; evolutionary game theory; benefit distribution; innovation contribution.
  DOI: 10.1504/IJADS.2026.10070990
   
  
• A graph-theoretical approach in dynamic decision-making analysis  
  by Henrik Kallio 
  Abstract: World has become complex in nature which means that decision-making happens more in dynamically changing conditions. This sets challenges as well as gives opportunities for research in economics. Big data gathered from decision-making behaviour opens up interesting avenues for studies and need to be investigated comprehensively. This requires development of new methodological approaches which are beneficial in analysing and building relevant theories. This paper presents a novel machine-learning methodology that is based on data mining with graph-theoretical innovation. The usefulness of methodology is demonstrated with data gathered from a dynamic business simulation game. Results show that the methodology can reveal new insights from the dynamic decision-making behaviour, for example centrality of decisions has the impact on the success in dynamic environment. The methodology developed in this paper may also be applied for practical purposes, for instance in order to study the usage of digital devices.
  Keywords: dynamic decision making; information systems; business simulations; economics; big data; machine learning; data mining; link mining; graph theory; decision tree analysis.
  DOI: 10.1504/IJADS.2026.10071402
   
  
• Impact of preservation, green technology in a sustainable inventory model with hybrid demand and pre payment discount policy under inflation  
  by S.R. Singh, Vaishali Chaudhary 
  Abstract: Environmental degradation is significantly impacted by carbon emissions and deterioration. Real-life demand functions vary, influenced by two distinct demand forms in the market, rather than remaining constant, linear, or nonlinear. This study addresses this complexity by developing a sustainable inventory model for deteriorating items with hybrid-price and stock sensitive demand under a carbon cap and trade policy. Green and preservation techniques are utilised to mitigate excess emissions and deterioration rates. Additionally, the model incorporates the concept of advance payment with a discount facility to accommodate fast-growing businesses. The analysis incorporates the effects of inflation and time-dependent holding costs, allowing for partially backlogged shortages. To validate the model, numerical analyses are conducted for three scenarios based on whether the demand function is hybridised or non-hybridised. Graphical and analytical representations demonstrate the concavity of overall profit, showing that the hybridized demand model yields higher profits compared to the non-hybridised model.
  Keywords: inflation; deterioration; preservation technology; green investment; advance payment; hybrid price and stock sensitive demand.
  DOI: 10.1504/IJADS.2026.10071511
   
  
• LSTM neural network combined with data mining techniques for financial crisis early warning model construction  
  by Jingwen Liu 
  Abstract: In enterprise financial management, early warning and prevention of financial crises are key to ensuring the long-term healthy development of the enterprise. The purpose of the research is to more effectively capture the potential factors of financial crisis and take corresponding preventive measures. Therefore, a new financial crisis warning model has been introduced in the study. This model combines the traditional K-means clustering algorithm with the improved Genetic Algorithm of Cross Model-K-means (CMGAK)for financial data pre processing. It is further augmented with the non-negative Garrote Relaxation long-short-term memory (NNG-LSTM) algorithm for comprehensive examination of financial data. The results showed that in the validation experiment of the effectiveness of the financial crisis warning model constructed by integrating the CMGAK algorithm with the NNG-LST M algorithm, the highest accuracy of the model's prediction in the multi-step dimension was92.54%in the two-year step sizes of T-2 to T-3.The financial crisis warning model, which combines the advantages of the CMGAK algorithm and the NNG-LSTM algorithm, has been demonstrated to perform particularly well in medium- and long-term forecasting. The model provides effective technical support for the prevention of financial crises in enterprises.
  Keywords: genetic clustering; LSTM; financial crisis; early warning model; data mining.
  DOI: 10.1504/IJADS.2026.10071918
   
  
• Formulating resource and capability configurations in complex contexts: a hybrid AHP-ANN approach for state-owned banks  
  by Mochammad Ridwan Ristyawan, Utomo Sarjono Putro, Manahan Siallagan, Arwan Ahmad Khoiruddin 
  Abstract: The pressing challenges posed by environmental crises require banks to rapidly devise innovative strategies. These strategic developments must be synchronised with the establishment of resource and capability configurations. Nonetheless, banks have exhibited a delayed response in crafting these configurations, particularly in complex scenarios, such as the COVID-19 pandemic. This study seeks to create a decision support system (DSS) prototype that leverages a hybrid analytic hierarchy process-artificial neural network (AHP-ANN), to design resource and capability configurations. A case study methodology is adopted, concentrating on a state-owned bank in Indonesia and employing a quantitative approach to simulate the DSS. Data were gathered through a purposive sampling of 107 participants. This study delineated the resource and capability configurations vital for strategy execution, encompassing human capital, financial capital, effective corporate governance, risk management, international digital networks, information technology, and organisational agility. The DSS proved effective in facilitating prompt decision making amidst the uncertainties of complex situations.
  Keywords: decision-making; management; bank; analytic hierarchy process; AHP; artificial neural network; ANN; resource; capability; decision support system; DSS; strategy; complex; preference judgment.
  DOI: 10.1504/IJADS.2026.10071940
   
  
• Adaptation of plant propagation algorithm for waste collection vehicle routing problem  
  by Nur Azriati Mat, Aida Mauziah Benjamin, Syariza Abdul-Rahman, Ku Ruhana Ku-Mahamud, Mohammad Fadzli Ramli 
  Abstract: Solid waste management (SWM) is an important service the government offers to residents of a country to manage generated residual waste. Failure to manage this waste can lead to unpleasant circumstances, such as environmental contamination and outbreaks of pest-borne diseases. Therefore, an efficient and cost-effective SWM system is required to improve the services. This research highlights one of the main issues of the SWM system, which is the waste collection vehicle routing problem (WCVRP). Essentially, this research addresses the adaptation of the plant propagation algorithm (PPA), which has never been considered in prior studies to resolve waste collection problems. The quality of the PPA solution was evaluated in terms of total travel distance, the number of vehicles/drivers required, the total working hours of drivers, and total fuel consumption. The proposed algorithm was tested on a WCVRP benchmark problem. Upon comparing PPA and other best-known solutions depicted in the literature, the solutions achieved on benchmark problems were extremely competitive.
  Keywords: waste collection; vehicle routing problem; benchmark problem; solid waste management; SWM; plant propagation algorithm; PPA.
  DOI: 10.1504/IJADS.2025.10063685
   
  
• Evolution of retailer's competitive performance considering price and service combination strategies: an agent-based simulation  
  by Zhen Li, Chongxin Tang, Yuqing Chen 
  Abstract: This paper explores an agent-based model that incorporates the Q-learning algorithm, and this model includes a competitive multi-agent retail-consumer interaction network. In the network model, various retail agents are constructed to compete for consumer groups under different network features (consumer neighbour nodes, consumer network reconnection probability, and consumer herding psychology intensity) with different pricing and service level combinations. All retail agent agents adjust their product prices and service levels under the Q-learning mechanism to maximise their expected sales and profits. Compared to previous studies, we make contributions that include, but are not limited to, constructing consumer networks with nodes of new network characteristics, as well as designing individual consumer characteristics with more complexity, including heterogeneous attributes such as the consumer's income level and the consumer's expectation level. The purpose of this paper is to provide recommendations for selecting the appropriate combination strategies for retailers in a complex market environment.
  Keywords: retailer competition strategy; dynamic pricing; service level; Q-learning algorithm; agent-based simulation.
  DOI: 10.1504/IJADS.2025.10069770
   
  
• An efficient approach to solve order batching, batch sequencing and picker routing problems simultaneously in warehouse operations  
  by Md. Saiful Islam, Md. Kutub Uddin 
  Abstract: Order picking is the most time-consuming and laborious part in warehouse operation. An efficient order batching approach may considerably enhance the effectiveness of the order picking process. In this research, a quadratic programming model is developed to solve the order batching, batch sequencing, and picker routing problems jointly. The objective is to minimise the sum of order processing and tardiness costs for a particular set of customer orders. The model is considered as an NP-hard problem. Therefore, as a solution methodology, a genetic algorithm (GA) based meta-heuristic approach is proposed to solve large-scale problems. A greedy routing technique is also adopted in the GA to estimate the optimal picking sequence for each batch. The effectiveness of the suggested meta-heuristic approach is compared with the earliest due date (EDD) order batching method. The experimental results show that the proposed GA-based approach generates promising results in an acceptable amount of computational time.
  Keywords: order picking; order batching; greedy routing policy; genetic algorithm; GA; warehouse management.
  DOI: 10.1504/IJADS.2025.10064103
   
  
• The capital structure determinants in small and medium-sized enterprises in the information technology sector  
  by António José Mendes Ferreira, Paulo Jorge de Almeida Pereira, Mário José Batista Franco, Dagoberto Ivo Sousa Couto dos Santos 
  Abstract: This study aims to analyse the relation between the determinants of capital structure and the level of debt in small and medium-sized enterprises (SMEs) in the information technology (IT) sector. The methodology adopted consists of applying a questionnaire to 100 IT SMEs in Portugal, followed by descriptive statistical analysis. The results obtained will provide managers and investors with valuable insights, highlighting the importance of factors such as firm size, asset tangibility, growth opportunities, business risk, profitability, age and tax benefits. The conclusion underlines that the relation between firm size and level of debt is complex, depending on contextual factors, and that pecking order theory influences financing decisions. The study fills a gap in the literature and contributes to developing the information technology sector in Portugal. The study refers to the main theories related to capital structure, such as the theory of Durand (1952), the approaches of Modigliani and Miller (1958, 1963), agency theory (Jensen and Meckling, 1976), trade-off theory (Myers, 1984) and pecking order theory (Myers and Majluf, 1984).
  Keywords: financial management; capital structure; small and medium-sized enterprises; SMEs; information technology; debt.
  DOI: 10.1504/IJADS.2025.10063772
   
  
• On the fringe of credit visibility: the value of alternative data for assessing the credit risk of subprime underbanked consumers  
  by Edwin Baidoo, Stefano Mazzotta 
  Abstract: In a modern economy, prospering without credit is difficult. Yet, Geraldes et al. (2022) report, for instance, that as many as 2.5 billion individuals in the world have little to no bank relationships. Referred to as underbanked consumers, they are unable to obtain credit due to their limited or non-existent credit history. Alternative data refers to data sources that are not traditionally used in credit scoring. Current research suggests that alternative data may contain predictive information helpful in assessing the creditworthiness of underbanked consumers. We use statistical and machine learning models to examine the value of alternative data for assessing the creditworthiness of the USA's subprime underbanked consumers. We use a proprietary dataset of automobile loans that includes both traditional and alternative data to compare the predictive value of each data type. Our main finding is that the informational content of alternative data is not subsumed by traditional data. In addition, we find that alternative data alone have value that can help lenders extend credit to subprime underbanked consumers, enabling them to fully participate in the mainstream economy.
  Keywords: alternative data; credit scoring; underbanked consumers; personal bankruptcy; auto loans.
  DOI: 10.1504/IJADS.2025.10066241
   
  
• A genetic algorithm model for route optimisation of cold chain product transportation using vehicles  
  by Sheng Zeng, Bing Wang, Gang Hu, Xu-sheng Hu, Xian-jun Dai 
  Abstract: Traditional cold chain logistics vehicles are suitable for short distance transportation, while long distance cold chain transportation faces more challenges; as the transportation distance increases, the time and temperature control in the cold chain link becomes more difficult. Because the driving route of the vehicle has been subject to the influence of technology, the driving route of the vehicle cannot be optimised. The traditional vehicle transportation is only for the tracking of the vehicle, the infrared sensor avoids obstacles to find the driving route of the vehicle, and the traditional driving route of the vehicle has limitations. At the same time, the genetic algorithm adds an adjustment strategy based on time window, which can effectively reduce the probability of conflict and deadlock, accelerate the convergence speed of the solution, and solve the scheme with the shortest total assembly time within the specified time. Based on the above design, in this paper, the vehicle path optimisation can shorten the transportation time, reduce the overall transportation cost, and improve the transportation efficiency.
  Keywords: transportation; genetic algorithm; GA; route optimisation; path; transportation cost.
  DOI: 10.1504/IJADS.2025.10064623
   
  
• Green finance to achieve environmental sustainability: a review and bibliometric analysis  
  by Ravita Kharb, Neha Saini, Shabani Bhatia, Charu Shri, Dinesh Kumar 
  Abstract: The concept of green finance has evolved over time in response to economies' aspirations. Green finance has captured the interest of academic scholars and policymakers owing to the growing global concern for environmental sustainability. It is a major initiative that the government and society take towards environmental sustainability. The current study aims to undertake a comprehensive bibliometric analysis and identify the facilitator of green finance across all economies. The intellectual framework and bibliography of the selected articles were examined using Biblioshiny. To ensure accuracy, several inclusion and exclusion criteria were applied. By examining 65 articles, the study also attempted to pinpoint the factors that facilitate and hinder green finance. This study is the earliest effort to understand the emergence of green finance and its driving factors. This study contributes significantly to the literature by identifying the enablers and barriers of green finance transformation towards green growth.
  Keywords: green finance; environmental sustainability; climate change; green growth; green innovation.
  DOI: 10.1504/IJADS.2025.10064181
   
  
• An integrated inventory model of deteriorating items with volume agility and time dependent demand under an imperfect production process  
  by Surendra Vikram Singh Padiyar, Deepa Makholia, S.R. Singh, Vipin Chandra Kuraie, Ummeferva Zaidi, Vaishali Singh 
  Abstract: This research paper presents an integrated inventory model that addresses the challenges arising from deteriorating items in the presence of volume agility and time-dependent demand for vendor and supplier under imperfect production process. By incorporating rework processes into manufacturing, businesses not only contribute positively to the green environment but also create a more sustainable and responsible approach to resource management. This model offers various critical aspects of inventory control, aiming to optimise the overall system performance and enhance decision-making in complex supply chain environments. The objective is to determine the optimal production volume to minimise the total cost over a specified planning horizon. To validate the results of the proposed model a numerical illustration has been done and the concavity of the objective function is shown using MATHEMATICA 12 software. Finally, to find some useful observations and managerial insights, a sensitivity analysis has also been done for various parameters.
  Keywords: volume agility; imperfect production; vendor; supplier; deterioration; time dependent demand.
  DOI: 10.1504/IJADS.2025.10065313
   
  
• Optimal planning of charging station for electric vehicle based on hybrid RODDPSO and K-means algorithm  
  by Birong Huang, Huahao Zhou, Fangbai Liu, Yuhang Zhu, Peng Geng, Xiaoyan Zhao 
  Abstract: This research proposes a hybrid approach combining K-means with randomly occurring distributedly delayed particle swarm optimisation (RODDPSO) to strategically locate electric vehicle charging stations (EVCS). The method is structured in three phases: initial regional clustering for demand analysis, refined selection of charging pile locations within these regions, and consolidation into efficient charging stations. The approach enhances the traditional K-means by optimising initial centroids with RODDPSO, mitigating the risk of suboptimal solutions due to local minima. The Yancheng ride-hailing dataset is employed to validate the model, showcasing a significant improvement in utilisation rates and operational efficiency compared to the standard K-means algorithm. The findings underscore the hybrid method's potential to optimise EVCS placement for enhanced service coverage and economic viability.
  Keywords: randomly occurring distributedly delayed particle swarm optimisation; RODDPSO; K-means; cluster centre optimisation; optimal planning of charging station.
  DOI: 10.1504/IJADS.2025.10065430
   
  
• Sensitised multivariate homogeneously weighted moving average charts for Phase II monitoring of the multivariate process mean  
  by Farshad Bahar, Mohammad Reza Maleki, Ali Salmasnia, Hossein Eghbali 
  Abstract: In some processes, the occurrence of assignable cause results in small disturbances in the process parameters. Moreover, a fundamental assumption in designing control charts is that the measurements are accurate. This article aims to enhance the multivariate homogeneously weighted moving average (MHWMA) chart with 2-of-2 and 2-of-3 sensitisation rules for the rapid detection of mean changes. Then, the proposed charts are developed based on additive covariate model to account for the impact of measurement errors. Finally, multiple measurement technique is utilised to reduce the error impact on run length of the sensitised charts. The performance of the proposed charts is assessed through extensive simulations. The results show that the MHWMA chart based on the 2-of-3 rule outperforms its counterpart which employs 2-of-2 rule. Secondly, the measurement error negatively affects the power of both the proposed charts. Thirdly, increasing the number of measurements for each product can improve the chart detectability.
  Keywords: multivariate homogeneously weighted moving average; MHWMA; measurements error; sensitisation rules; multiple measurements; additive covariate model.
  DOI: 10.1504/IJADS.2025.10066477
   
  
• Cold chain logistics monitoring and logistics vehicle scheduling optimisation  
  by Sheng Zeng, Bing Wang, Xian-jun Dai 
  Abstract: With the development of China's economy, the per capita disposable income of residents has been constantly increasing, and the cold chain logistics industry has ushered in a brand-new development period. However, problems related to the transportation and monitoring of cold chain logistics have also emerged frequently. On the basis of the current situation, the purpose of this thesis is to offer an intelligent monitoring platform for cold chain logistics to monitor its vehicle driving path and vehicle scheduling. Secondly, due to the different routes of vehicles in the process of driving, the ant colony algorithm is taken to optimise the routes and improve the ant colony algorithm in such cases and it shows that the updated ant colony algorithm is superior to the path of the conventional ant colony algorithm. The findings of the experiment indicate that the optimisation rate can be increased by about 4% through the four route optimisation methods. The improved algorithm significantly shortens the path, reducing the cost of cold chain logistics transportation and promotes the advancement mode of traditional cold chain logistics transportation companies. It provides a basis for the subsequent logistics optimisation, and continuously improves the efficiency and quality of cold chain logistics. The contribution of intelligent monitoring and route optimisation of cold chain logistics is to improve transportation efficiency, reduce costs, ensure product quality and safety, and provide important support for the development and progress of modern logistics industry.
  Keywords: cold chain logistics; monitoring platform; ant colony algorithm; path; cost.
  DOI: 10.1504/IJADS.2025.10067857