International Journal of Applied Decision Sciences (33 papers in press)

Regular Issues

• Pricing strategies in a risk-averse dual-channel supply chain with manufacturer services  
  by Zhenhua Yang, Lin Huang, Yu Xia, Jian Guo Liu 
  Abstract: This paper studies a dual-channel supply chain consisting of one risk-averse manufacturer and one risk-averse retailer with stochastic demand. Herein, the manufacturer provides value-added services to enhance channel demand. First, the optimal pricing and service decisions of the channel members are investigated under different settings, i.e., the cooperative game, Bertrand game, and manufacturer Stackelberg (MS) game models. Second, the effects of channel members’ risk aversion on optimal channel prices and expected utilities are analysed under the assumption that the manufacturer service is a decision variable and an exogenous variable, respectively. Third, sensitivity analysis and numerical simulation are performed to verify our propositions consistently and seek more managerial implications. The findings suggest that the manufacturer’s value-added services in his direct channel will improve the direct price while decreasing the retail price. Consumers’ channel loyalty degree has a great influence on the optimal price decisions and the performance of the channel members. The direct price increase while the retail price decrease in the manufacturer’s value-added services. The retailer’s risk aversion has a greater influence on her own price decisions than that of the manufacturer.
  Keywords: dual-channel supply chain; risk aversion; value-added services; Bertrand game; Stackelberg game.
  DOI: 10.1504/IJADS.2024.10057611
   
  
• Channel competition, manufacturer incentive and supply chain coordination  
  by Zhi Pei, John Wang, Barbara Ross Wooldridge, Jie Sun 
  Abstract: COVID-19 created a surge in e-commerce usage, leading to fierce channel competition between the manufacturer’s online sales and the offline retailer. Hence, the imperative need for effective and innovative optimisation strategies to mitigate channel competition. Manufacturer-coupons are widely practiced in market, yet research on the importance they play in coordinating channel competition to achieve optimisation in channel distributions is scarce. This research addresses this gap by examining the effectiveness of manufacturer-coupons on the coordination of the manufacturer’s online sales and offline retailer’s sales. The findings indicate that issuing a manufacturer-coupon to the customers who buy from the offline retailer reduces the competition in the different channel distributions, but cost sharing of the retailer coupon is a better strategy. We thus examine if profit sharing is an effective strategy to facilitate the use of manufacturer-coupon in the market. After comparing different scenarios, we conclude that advanced profit-sharing can be effective in making manufacturer-coupon prevalent in the market and thus alleviate channel competition effectively.
  Keywords: e-commerce; channel competition; manufacturer-coupon; coordination strategies; game theory.
  DOI: 10.1504/IJADS.2024.10058177
   
  
• A DEA-based decision framework for performance evaluation and ranking of workers in a real case from food industries  
  by Sadegh Niroomand 
  Abstract: In this study, a performance evaluation problem of operational employees in a real case study is considered. The case study is a firm from food industries where different tomato pastes are produced there. In addition, further than performance evaluation, in this study the operational employees are ranked based on their performances. As the main contribution of this study, a real-world problem from food industries is focused and solved by the optimisation-based approaches. For this aim, the most important criteria from the literature such as salary, work conditions, responsiveness, motivation, and productivity are selected. A decision framework based on data envelopment analysis (DEA) is used. For this aim, the Russel DEA model is used as the first stage to evaluate the performance of each operational worker (DMU) by determining efficient and inefficient DMUs. As the second stage, an Anderson-Peterson approach based on a modification of the Russel model is proposed to rank the DMUs based of their performances. Finally, based on the obtained results, some comparisons with the other DEA models of the literature is performed and the managerial insights are presented.
  Keywords: food industry; performance evaluation; performance ranking; data envelopment analysis; DEA; Russel model.
  DOI: 10.1504/IJADS.2024.10058382
   
  
• A new model for efficiency estimation and evaluation:DEA-RA-Inverted DEA model  
  by B.-J. Zhang, Shi-Lan Su, Qizhou Gong 
  Abstract: Data envelopment analysis (DEA) is widely used in various fields and for various models. Inverted data envelopment analysis (inverted DEA) is an extended model of DEA. Regression analysis (RA) is a statistical process for estimating the relationships among variables based on the model of averaged image. There are no essential relations among DEA and RA and inverted DEA. We creatively combine DEA, RA and inverted DEA to propose a new model: DEA-RA-Inverted DEA model. The model realises the efficiency estimation and evaluation through a discussion of the residual variables and the residual ratio coefficients. In addition, we will demonstrate the effectiveness of the model by applying it to efficiency estimation and evaluation of 16 Chinese logistics enterprises.
  Keywords: data envelopment analysis; DEA; inverted data envelopment analysis; inverted DEA; regression analysis; efficiency evaluation; sample decision unit; preference cone.
  DOI: 10.1504/IJADS.2024.10058443
   
  
• A Fuzzy-probabilistic Bi-objective Mathematical Model for Integrated Order Allocation, Production Planning, and Inventory Management  
  by Solikhin Solikhin, Sutrisno Sutrisno, Abdul Aziz, Purnawan Adi Wicaksono 
  Abstract: An optimisation-based decision-making support is proposed in this study in the form of fuzzy-probabilistic programming, which can be used to solve integrated order allocation, production planning, and inventory management problems in fuzzy and probabilistic uncertain environments. The problem was modelled in an uncertain mathematical optimisation model with two objectives: maximising the expectation of production volume and minimising the expectation of total operational cost subject to demand and other constraints. The model belongs to fuzzy-probabilistic bi-objective integer linear programming, and the generalised reduced gradient method combined with the branch-and-bound algorithm was utilised to solve the derived model. Numerical simulations were performed to illustrate how the optimal decision was formulated. The results showed that the proposed decision-making support was successful in providing the optimal decision with the maximum expectation of the production volume and minimum expectation of the total operational cost. Therefore, the approach can be implemented by decision-makers in manufacturing companies.
  Keywords: decision-making support; fuzzy parameter; order allocation; probabilistic parameter; production planning; uncertain programming.
  DOI: 10.1504/IJADS.2024.10058528
   
  
• Behavioural Finance Research and Knowledge Mapping: A Comprehensive Bibliometric Analysis from 2010 to 2022  
  by Anshita Bihari, Manoranjan Dash, Padma Charan Mishra, Sukumar Dash 
  Abstract: The study aims to explore the patterns and connections in the behavioural biases and investment decisions of existing literature on the Web of Science database using Science mapping and performance analysis tools. This study selected 512 research papers from the Web of Science database published between 2010 and 2022 after deep screening all influential authors with their citations exposed along with top journals. The pattern of the papers highlighted and the connection between literatures gives direction for future research. Publication on behavioural biases and investment decisions increased since 2016. The Journal of Behavioural Finance is leading in published documents, the Journal of Financial Economics has the highest citation count, and the USA is the top country in publications and citations. The outcome of this study provides valuable insights into the intellectual structure of biases of investors and adds value to the existing knowledge. This review offers knowledge and theories for the behavioural finance discipline and provides a road map for the future trend of research on behavioural biases and investment decisions.
  Keywords: knowledge mapping; behavioural biases; investment decision; bibliometric analysis.
  DOI: 10.1504/IJADS.2024.10058529
   
  
• Does the optimal model always perform the best? A combined approach for interval forecasting
  by Zhe Zhang, CHOO W.E.I. CHONG, Jayanthi Arasan 
  Abstract: Interval forecasting is widely applied by decision makers for it can provide more comprehensive information. In the literature, GARCH models under different distributional assumptions are applied and evaluated to find the optimal interval forecasting model for the experimental data. However, the optimal model selected based on sample data from a specific period may not always perform the best in future periods. Therefore, this study employs GARCH models based on different distributional assumptions for interval forecasting of the daily return data of the Nasdaq Composite Index. The results show that the forecasting performance of some models exhibits significant differences across different periods. To address this issue, this study proposes a Monte Carlo-based non-parametric interval forecasting combination method. The results demonstrate that this method can effectively avoid the risk of forecasting inaccuracies caused by relying on a single model.
  Keywords: interval forecasting; optimal model; combined approach; GARCH model; distribution assumptions; Monte Carlo.
  DOI: 10.1504/IJADS.2025.10058959
   
  
• Sentiment Analysis on Stocks: A Hybrid Feature Extraction Technique on Fourteen Classifiers
  by MEERA GEORGE, R. Murugesan 
  Abstract: Accurately predicting stock prices is challenging and has garnered massive attention from researchers and investors alike. Though the literature has shown sentiment analysis as a promising approach for efficient stock price prediction, it has found a considerable gap in studies using multiple feature extraction techniques with hybrid models for the efficient sentiment classification. Under these circumstances, this study aims to perform sentiment analysis using five feature extraction techniques including a hybrid and 14 classifiers for the accurate classification of stock tweets. The study extracted 21121 tweets spanning March 2022 to December 2022 using Twitter application programming interface. The empirical result shows the superiority of the hybrid feature extraction technique over the other methods. The support vector machine classifier with a hybrid feature extraction technique is found to be the best-performing sentiment analysis model for Twitter stock data. The study has potential applications in building optimal investment strategies and decision-making.
  Keywords: stock price; sentiment analysis; classifiers; feature extraction; hybrid.
  DOI: 10.1504/IJADS.2025.10059408
   
  
• Environmental Disclosure and Firm Performance: Current State and Future Avenues
  by Rubina Michela Galeotti, Daniela Cicchini, Fabiana Roberto 
  Abstract: This study aims to review the literature on environmental disclosure and firm performance in order to show current trends, challenges, and adventures for future research. We attempted a systematic and structured literature review of 67 studies published in 2001-2023 using the accounting journal in the Chartered Accounting Business School (CABS) ranking. The analysis revealed three main research streams: 1) firm performance, CSR and sustainability; 2) accounting and environmental disclosure; 3) board of directors and decision-making process in the field environment and firm performance. The relevance of this literature review is due to understanding the state of the art between environmental disclosure and firm performance. Through our work, we highlight the current and future issues in the firm performance and environmental disclosure. This paper supports future research and it is directed to academics, practitioners, policy makers and decision makers.
  Keywords: environmental disclosure; firm performance; accounting; sustainability.
  DOI: 10.1504/IJADS.2025.10059810
   
  
• Predictors of purchase intention in gastronomic establishments in the city of Medellin
  by Rodolfo Casadiego, Isabella Marín-Cuartas, Cristian Santiago Toro-Herrera, Alejandro Silva-Cortés, Marianella Luzardo-Briceño 
  Abstract: The pandemic caused by COVID-19 adversely impacted the gastronomy sector, affecting company-customer interactions. This research aimed to identify the variables influencing purchase intentions within Medellin’s gastronomic establishments, for this, a quantitative exploratory approach was conducted, and 418 consumers were surveyed. Data analyses were conducted via RStudio, entailed confirmatory factor analysis for construct validation and structural equation modelling to examine causal relationships. As results factors like hedonic value, user-generated content, perceived risk, corporate image, perceived quality, attitude towards the restaurant, and restaurant type were assessed for their impact on purchase intention. Notably, while the perceived quality was statistically significant, it is inversely correlated with purchase intention, implying a diminished inclination to dine upon perceiving quality decline. These insights provide a comprehensive understanding of consumer decision making during the pandemic, facilitating strategies for optimal market positioning amidst evolving demographic-cultural dynamics and business chaos. This assessment has broader implications than the gastronomic domain.
  Keywords: hedonic value; perceived quality; purchase intention; restaurant type; structural equation modelling; SEM.
  DOI: 10.1504/IJADS.2025.10060198
   
  
• A Deep Learning Approach Using Modified Xception Net for Oral Malig-nancy Detection Using Histopathological Images of Oral Mucosa  
  by Madhusmita Das, Rasmita Dash 
  Abstract: The early detection of oral malignancy by physicians is a strenuous task. The analysis of histopathological oral malignancy images using image processing and deep learning techniques can be an add-on facility for doctors to diagnose oral cancer. In this work, a deep learning model is used, designing a modified Xception net with swish activation function and generalised mean pool for the detection of oral malignancy. To prove the superiority of the model, three stages of comparative analysis are carried out. In the first stage, the model is compared with a few advanced models explicitly Alexnet, Resnet50, Resnet101, VGG16, VGG19, Inception net and original Xception. In second stage, loss and accuracy graphs analysis is done and in the third stage, proposed model’s accuracy is compared with other model’s accuracy available in the literature. It is found that the modified Xception net got upgraded performance by an accuracy of 98.97%.
  Keywords: Xception net; histopathological oral image; deep learning; swish activation function; oral cancer.
  DOI: 10.1504/IJADS.2024.10060616
   
  
• Optimal Planning of Electric Vehicle Charging Facilities Considering Demand Stimulus Effects
  by Yongzhong Wu, Zhi Jie Zhu, XIANGYING CHEN, Huihui Chen 
  Abstract: The development of electric vehicle charging facilities can positively influence user demand and demand landscape, a factor often neglected in existing models that focus on current demand and organic growth. We present models for determining the quantity and location of charging facilities while considering their impact on future demand landscape. Employing a gravity model, we analyse the interaction between charging station network planning and future demand. We develop an optimisation model for charging facility planning, aiming to minimise total social costs. Our proposed solution employs the weighted Voronoi polygon graph algorithm. Through a case study, we demonstrate the significance of the proposed model by comparing the solution obtained that considers the impact of charging network construction on user demand with a solution that neglects this stimulus. The results underscore the importance of incorporating user demand stimulus in infrastructure planning, providing valuable insights for electric vehicle charging facility investors and operators.
  Keywords: electric vehicles; charging facilities; facility siting; gravity model; total social costs.
  DOI: 10.1504/IJADS.2025.10060814
   
  
• An MINLP Model for Project Scheduling with Feeding Buffer  
  by Ashkan Ayough, Masood Rabieh, Mohammad Naeem Javadi, Behrooz Khorshidvand 
  Abstract: This study addresses a critical chain project scheduling (CCPS) problem regarding the feeding buffer. The main contribution of this study lies in determining the critical chain when the feeding buffer is considered along with the project buffer, a less addressed issue in the critical chain literature. Using a mixed-integer nonlinear programming (MINLP) model, the critical chain of a project with no break-down and no overflow is found. Moreover, the impact of feeding buffer on the criticality of activities is discussed. The problem is solved using the Lingo software package for validation in small-sized instances. Since the CCPS is known as an NP-hard problem, a genetic algorithm (GA) is also designed to solve large-scale instances. The algorithm’s performance is confirmed using various project scheduling library test problems. Sensitivity analysis is implemented based on some crucial parameters, and the critical chain is analysed after conducting several experiments. It is shown how considering the feeding buffer makes different critical chains and how shortlisting activities and resources are optimally managed.
  Keywords: critical chain project scheduling; CCPS; feeding buffer; genetic algorithm.
  DOI: 10.1504/IJADS.2024.10060921
   
  
• Development trust among customer and producer Inventory Model, when demand dependent on selling price with shortage under the environment of uncertainty
  by S. V. Singh Padiyar, Kanchan Joshi, S. Pundir, Dipti Singh 
  Abstract: Mostly it is observed that when individual inventory model is given importance then demand is either constant or time dependent but at the same time selling value of inventory cannot be ignored. Generally. For many consumer products, it is observed that the selling price of an item greatly influences the rate of consumption. Due to which the demand of the product may vary with the selling price, also it is necessary to check the reliability of the inventory maintained during production. And when the demand of the product depends on the selling price and is favorable to the customer, then the possibility of shortage is also high. Therefore, it is necessary to give importance to the impact of selling price and shortage while developing the inventory model. Along with this many types of uncertainties arise during the production and to overcome these uncertainties the model is developed by making fuzzy model and graded mean representation is used to deform the total cost function of the system.
  Keywords: Imperfect production; selling price demand; time dependent deterioration; shortage; fuzzy environment.
  DOI: 10.1504/IJADS.2025.10061130
   
  
• Triple Voting: Hybrid Cardiovascular Diseases Prediction Model
  by Dahlak Daniel Solomon, Karan Aggarwal, Sonia Sonia, Kushal Kanwar, Kemal Polat 
  Abstract: Currently, cardiovascular diseases are a high-risk cause of death in both developed and developing countries. Thus, heart disease prognosis has received substantial interest in the medical field worldwide. The incidence of heart disorders is escalating at an alarming rate, and it is crucial and worrisome to anticipate their occurrence. Predicting and detecting cardiovascular disease using machine learning and data mining might be clinically useful, but difficult. There are numerous machine learning algorithms accessible, several studies have developed machine learning algorithms for early cardiac disease prediction to help physicians suggest medical treatments. The accuracy of the model will be evaluated to determine whether the performance of the model is accurate or not. Seven machine learning methods are compared in this study, with the data obtained from the UCI Laboratory’s cardiovascular patient database. In essence, this research presents a majority voting-based hybrid model which is called triple voting. The hybrid model uses voting of Naive Bayes, logistic regression (LR) and support vector machines (SVM) experimental outcomes show the proposed triple voting model’s accuracy is 89%, which is higher than the individuals and other proposed hybrid models.
  Keywords: cardiovascular disease; machine learning; majority voting; ensemble learning.
  DOI: 10.1504/IJADS.2025.10061195
   
  
• An Inventory Model with Piecewise Cost Functions and Multivariate Deterioration  
  by Shilpy Tayal 
  Abstract: The study highlights that the temperature of surroundings affect the rate of deterioration and deterioration is a major parameter to check the quality of any product. Here an inventory model for routine items with time and temperature dependent deterioration and piecewise cost functions has been developed. Combining the above mentioned factors together and considering all possible cases of temperature the optimal values of total average cost has been discussed at different points in the assumed range. With the help of numerical example it is concluded that total average cost is minimum at that temperature value which is feasible for that particular item. With numerical analysis the optimality of the system in all the three cases has also shown graphically. Further to verify the system stability, sensitivity analysis has been performed and the system is found to be quite stable.
  Keywords: Temperature and Time Dependent Deterioration; Piecewise Cost Functions; Demand; Inventory; Shortages; Partial Backlogging; Lost sale.
  DOI: 10.1504/IJADS.2025.10061559
   
  
• A Financial Auditing Approach Using Failure Effect Mode Analysis  
  by Saeed Askary, Davood Askarany, Yusuf Joseph Ugras 
  Abstract: This paper proposes adopting Failure Effect Mode Analysis (FMEA) in audit procedures in order to improve the prediction of corporate failures and improve reliance on financial reports. It is well known that corporate failures adversely affect the accounting profession’s reputation, public interest, social costs, capital markets, and the national financial and monetary economic system, causing wasted resources by companies and losses by shareholders. In this article, we incorporate various recommendations of Brydon (2019) by suggesting the use of FMEA to strengthen the auditing profession. We are focusing on fundamental financial measures of liquidity and profitability, intending to increase the reliability of going concern issues for the company being audited. This article proposes the FMEA model for a substantial audit reform. FMEA adoption can prevent corporate failures in the future through the proposed model. This research is helpful to standard setters, managers, auditors, and governmental agencies and, finally, can protect the public interest.
  Keywords: corporate failures; failure mode analysis; going concern; liquidity; profitability.
  DOI: 10.1504/IJADS.2025.10061883
   
  
• Empirical evidence of a preference for uncertainty in intertemporal risky prospects  
  by Viviana Ventre, Roberta Martino, Francesco Panico, Laura Sagliano, Luigi Valio, Luigi Trojano 
  Abstract: Recent studies suggest that uncertainty should be added to the risk to evaluate financial decisions. The present study examines the interaction between uncertainty and subjective probability in risk evaluation. Subjective probability relates to an individual’s confidence in event occurrence. To explore the impact of different temporal perspectives on the perception of probability, random probabilities are assessed over different time frames. Empirical data and modelling validate that subjective probability varies over time, aligning with the non-rationality behaviour observed in hyperbolic discounting. Notably, applying the belief function theory formalises the link between a steeper discount function and choosing ‘larger later’ options in risky intertemporal prospects. Remarkably, results unveil a ‘preference for uncertainty’, where individuals exhibit greater patience in pursuing rewards. This experimental approach improves the qualitative and quantitative understanding of the risk-uncertainty dynamic.
  Keywords: behavioural finance; delay discounting; financial decision-making; FDM; hyperbolic discounting; impatience; imprecise probability; intertemporal choice; intertemporal prospect theory risk.
  DOI: 10.1504/IJADS.2025.10062032
   
  
• An optimization model of time-of-use pricing for ride-hailing platforms  
  by Wei Zhang, Shujing Wan 
  Abstract: For the problem of online ride-hailing market pricing, time-of-use pricing plan on ride-hailing platform is studied in this paper, considering the interests of the platform, drivers and passengers. Firstly, the multi-objective optimisation model of time-of-use pricing is built, in which the different price coefficients are used in peak hours and off-peak hours. The method of determining the drivers-passenger matching quantity is proposed. Then the algorithm solving the model is designed based on non-dominated sorting genetic algorithms. Finally, the validity of the time-of-use pricing method proposed in this paper is verified by a case study, and the relevant rules of time-of-use pricing are analysed. The research shows that the method can effectively improve the interests of the platform, driver and passenger. The revenues of the platform and driver can be increased by 12.9% and 4.15%, respectively, and the passenger payment can be saved by 8.64% at most relative to single price.
  Keywords: urban traffic; time-of-use pricing; multi-objective optimisation; online ride-hailing; genetic algorithm.
  DOI: 10.1504/IJADS.2025.10062959
   
  
• Adapting CRISP-DM to model enteric fermentation emission: farm level application  
  by Philippe Belmont Guerrón, Maria Hallo, Sergio Lujan-Mora 
  Abstract: Enteric fermentation contributes substantially to greenhouse gas emissions (GGEs) in agriculture, but may be reversible in the short-term. To date, numerous attempts have been made to model the environmental impact of agriculture, but have failed to integrate multiple dimensions of production. The objective of this study is to adapt the cross industry standard process for data mining (CRISP-DM) at farm level, using the concept of life cycle assessment (LCA) and implemented a modified version of the global livestock environmental assessment model (GLEAM). Using local data collected over 20 years and secondary data, our results show that for dairy cattle, the methane emissions factor from cattle is lower among marginal farms 86 Kg CH⁴ head⁻ ¹ year⁻ ¹ compared to semi-intensive and intensive farms across time and geographical regions (107.4 and 113.5 respectively) and demonstrate that this type of application is relevant for developing countries and smallholder agriculture, where production data is often unavailable.
  Keywords: CRISP-DM; design science research; DSR; model integration; data mining; GLEAM; life cycle assessment; LCA; agriculture data; enteric fermentation.
  DOI: 10.1504/IJADS.2025.10063368
   
  
• 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
   
  
• 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, Mario 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
   
  
• 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
   
  
• 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
   
  
• 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, 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 can not be optimised, and 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
   
  
• 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 address 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 for 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 optimize the overall system performance and enhance decision-making in complex supply chain environments. The objective is to determine the optimal production volume to minimize 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 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
   
  
• Research on influencing factors of value co-creation in product service system development based on Z-DEMATEL-ISM model  
  by Yang Xu, Xiuli Geng 
  Abstract: Product service system (PSS) is an integrated set of products and services with value co-creation as the core. Therefore, a profound understanding of factors and interrelationships that influence value co-creation in PSS development is essential for manufacturers. This paper proposes a systematic framework to analyse the influencing factors of value co-creation in PSS development. First, 22 factors are determined through the literature research and Delphi method. Second, decision-making trial and evaluation laboratory (DEMATEL) is adopted to identify the interactions between readiness factors, while Z number is introduced to capture the ambiguity and uncertain information in the decision-making process. Finally, a six-level hierarchical structure is determined based on interpretive structural model (ISM) to demonstrate the complex interrelationships among factors. The proposed framework is illustrated by taking the case of PSS value co-creation in Haier Group. The results indicate intelligent level being the most important factor of value co-creation in PSS development, followed by innovation potential and customer attraction. This finding could help manufacturers who aim for value increment to take appropriate steps to realise value co-creation of PSS.
  Keywords: product service system; PSS; value co-creation; influencing factors; Z number; DEMATEL.
  DOI: 10.1504/IJADS.2024.10056372
   
  
• Exploring intention to use cryptocurrencies payment across age groups and gender: a multi-stage machine learning application on the extended UTAUT model  
  by Dong Ling Tong, P.C. Lai, Ewilly Jie Ying Liew 
  Abstract: Given the increasingly widespread use of smartphones for cashless payment, the potential for using cryptocurrencies as an alternative e-payment asset becomes attractive. Promoting blockchain-based cryptocurrencies for e-payment faces challenges in payment conversion latency, where hard-earned crypto coins are not a recognised asset for actual payment. This study investigates users' intention to use cryptocurrencies in daily e-payment transactions. A multi-stage artificial intelligence-based analysis pipeline was employed to identify key factors of crypto-based e-payment usage and model the interaction between these factors. Experiments were conducted based on different target criteria, and factors corresponding to these target criteria were evaluated. Results show that platform reliability, improved usefulness, user-friendliness, and self-efficacy were directly associated with users' intention to use crypto coins for e-payment services. Age and gender differences were also evaluated across factors affecting users' intentions to use the new technology. Implications for management and cryptocurrency coin providers were discussed.
  Keywords: blockchain; cryptocurrency coin; e-payment; artificial intelligence; genetic algorithm; artificial neural network; ANN; decision tree; user intention; UTAUT model; decision making.
  DOI: 10.1504/IJADS.2024.10056311
   
  
• A comparative study of interval forecasting using GARCH models under symmetric and asymmetric distributional assumptions  
  by Zhe Zhang, Wei Chong Choo, Jayanthi Arasan 
  Abstract: Interval forecasting provides decision-makers with a range of possible future values, along with associated probabilities, which allows for a more informed decision-making process. Although GARCH models under different distributional assumptions are commonly compared for their volatility forecasting performance, their performance in interval forecasting is rarely discussed. This study aims to fill this gap by comparing the interval forecasting accuracy of GARCH models under symmetric and asymmetric distributions. SGARCH, EGARCH, and GJR-GARCH models under normal, student-t, GED distributions, and their skewed extensions are applied for one-day-ahead rolling interval forecasting on five major European and American stock indices: S&P 500, FTSE 100, CAC 40, DAX 30 and AEX. The average Winkler score (AWS) is used to measure the accuracy of interval forecasting. The conclusions of this study can be summarised as follows: In pairwise comparisons, the GARCH models under asymmetric distributional assumptions have better interval forecasting accuracy than the GARCH models under symmetric distributional assumptions. In comparisons among GARCH-type models, GJR-GARCH has better interval forecasting accuracy than SGARCH and EGARCH, while SGARCH and EGARCH exhibit similar interval forecasting performance.
  Keywords: comparative study; interval forecasting; GARCH models; symmetric distributions; asymmetric distributions; distributional assumptions; conditional variance; average Winkler score; AWS.
  DOI: 10.1504/IJADS.2024.10056334
   
  
• Reliability optimisation of the supply chain system based on seru production  
  by Zhe Zhang, Yalin Li, Lili Wang 
  Abstract: This paper focuses on the manufacturers implementing seru production, and constructs an integer programming model aiming at maximising the reliability of supply chain system based on considering the supplier's on-time supply rate of parts, worker assignment, and production plan comprehensively. In view of the complexity of proposed problem, a hybrid search strategy is designed by combining genetic algorithm (GA) and variable neighbourhood search (VNS), i.e., a hybrid genetic algorithm with variable neighbourhood search (HGAVNS) algorithm based on the multi-level encoding method. To verify the advance of proposed HGAVNS algorithm, the comparison with GA and VNS is presented, and experimental results show that HGAVNS performs better in solving proposed problem.
  Keywords: seru production; supply chain reliability; genetic algorithm; variable neighbourhood search; hybrid algorithm.
  DOI: 10.1504/IJADS.2024.10060920
   
  
• A novel IoT-based arrhythmia detection system with ECG signals using a hybrid convolutional neural network and neural architecture search network  
  by Ümit Şentürk, Ceren Gülen, Kemal Polat 
  Abstract: Electrocardiogram (ECG) signals are the most common tool to evaluate the heart's function in cardiovascular diagnosis. Irregular heartbeats (arrhythmia) found in the ECG play an essential role in diagnosing cardiovascular diseases (CVD). In this paper, we proposed an arrhythmia classification method with the neural architecture search network (NASNet) model, an optimised version of the convolutional neural networks (2D-CNN) model, which performs very well in visual information analysis classification. We aimed to use the arrhythmia classification problem in IoT and mobile devices with the NASNet model with low parameter numbers and processing capacity without performance loss. 2D input data have been obtained by converting the classified heart rate signals in the datasets into image files. The 2D data obtained have been classified by machine learning, CNN, and NASNet models. As a result of classification, 2D CNN accuracy was 97.51%, and the NASNet model was 96.89% accuracy. As a result of arrhythmia classification, the accuracy rates of the NASNet and the 2D CNN models were close. In conclusion, the proposed IoT-based arrhythmia detection system with ECG signals using a hybrid CNN and NASNet is a promising tool for the early detection of arrhythmias. Furthermore, it could help to reduce the mortality associated with these potentially fatal conditions.
  Keywords: arrhythmia detection; neural architecture search network; NASNet; IoT; network optimisation; time series; real-time measurement.
  DOI: 10.1504/IJADS.2024.10057677
   
  
• Cognitive technology in human capital management: a decision analysis model in the banking sector during COVID-19 scenario  
  by Senthil Kumar Arumugam, Joji Abey, R. Seranmadevi, Shefali Srivastava, Ashish Dwivedi 
  Abstract: Cognitive technologies are products of the artificial intelligence (AI) domain which execute tasks that only humans used to perform. The impact of cognitive technologies on the management of human capital (HC) has a massive effect in the banking sector. This paper studies the transformation of cognitive technology to human capital management (HCM) in the banking sector during the COVID-19 pandemic. The study draws data from 201 bank employees working in private, public, and foreign banks using a multi-stage sampling method in India. A number of hypotheses were framed and tested using multivariate and regression analyses. The results from the study indicate a significant change in the performances of bank employees statistically during the transformation of cognitive technologies. Cognitive technologies such as payment, product customisation, self-services, workload alleviation, automated back-office function, and a personalised experience significantly contribute to the HCM.
  Keywords: artificial intelligence; AI; banking; cognitive technologies; COVID-19; human capital management; HCM; machine learning; ML.
  DOI: 10.1504/IJADS.2024.10057610