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International Journal of Applied Decision Sciences

International Journal of Applied Decision Sciences (IJADS)

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International Journal of Applied Decision Sciences (27 papers in press)

Regular Issues

  • Adaptation of plant propagation algorithm for waste collection vehicle routing problem   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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
     
  • On the fringe of credit visibility: the value of alternative data for assessing the credit risk of subprime underbanked consumers   Order a copy of this article
    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 United States’ subprime underbanked consumers. We use a proprietary data set 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
     
  • Sensitized multivariate homogeneously weighted moving average charts for Phase II monitoring of the multivariate process mean   Order a copy of this article
    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
     
  • Optimising online order batching operations in a manual order picking warehouse: a genetic algorithm approach   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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
     
  • Cold chain logistics monitoring and logistics vehicle scheduling optimisation   Order a copy of this article
    by Sheng Zeng , Bing Wang, Xian-jun Dai 
    Abstract: Because of its development, cold chain logistics is gradually concerned by the public and the transportation and monitoring problems of cold chain logistics frequently appear. 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
     
  • Digital entrepreneurship: the influence of social media on consumer buying behaviours   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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
     
  • 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 avenues 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
     
  • A deep learning approach using modified Xception net for oral malignancy detection using histopathological images of oral mucosa   Order a copy of this article
    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 the second stage, loss and accuracy graphs analysis is done and in the third stage, the 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
     
  • Development of trust among customer and producer inventory model, when demand is dependent on selling price and with shortage under the environment of uncertainty
    by Surendra Vikram Singh Padiyar, Kanchan Joshi, S.R. Singh, Dipti Singh 
    Abstract: Mostly, it is observed that when the individual inventory model is given importance, then demand is either constant or time dependent, but at the same time, the selling value of the 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 favourable 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 a fuzzy model. Numerical examples and sensitivity analysis of key parameters are provided in order to investigate the effect on the optimal total cost of the system.
    Keywords: imperfect production; selling price demand; time dependent deterioration; shortage; fuzzy environment.
    DOI: 10.1504/IJADS.2025.10061130
     
  • Advanced statistical methods for real-time industrial process analysis: an analysis of the literature   Order a copy of this article
    by Pedro Vaz, Ana Cristina Braga, Maria do Sameiro Carvalho 
    Abstract: Real-time work and data-driven (DD) strategies are gaining popularity in Industry 4.0, which highlights the importance of revising the statistical methods applied in these environments. This study is the first systematic literature review on advanced statistical methods for real-time industrial process analysis (ASMs-RTIPA), offering valuable insights for future research by compiling existing recent studies (from 2014 to 2020) systematically. The review indicates a lack of publications on ASMs-RTIPA, yet it supports its application. Approximately 41% of the selected publications use case studies, 23% develop models, and 18% are conceptual. 'Advanced process control' is the most common keyword in the publications studied. The majority of publications come from the USA, UK, Germany, and the Netherlands. Engineering, generally, has the highest concentration of publications on the subject.
    Keywords: advanced statistical methods; ASMs; real-time industrial process analysis; advanced process control; process improvement; systematic literature review; SLR.
    DOI: 10.1504/IJADS.2025.10067733
     
  • Optimal planning of electric vehicle charging facilities considering demand stimulus effects
    by Yongzhong Wu, Zhijie Zhu, Xiangying Chen, Huihui Chen 
    Abstract: The development of electric vehicle charging facilities can positively influence user demand and the 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 the 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
     
  • Triple voting: hybrid cardiovascular diseases prediction model
    by Dahlak Daniel Solomon, Karan Kumar, 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 Naïve 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 individual models 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   Order a copy of this article
    by Shilpy Tayal 
    Abstract: The study highlights that the temperature of surroundings affects 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
     
  • Empirical evidence of a preference for uncertainty in intertemporal risky prospects   Order a copy of this article
    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; subjective probability; uncertainty.
    DOI: 10.1504/IJADS.2025.10062032
     
  • Adapting CRISP-DM to model enteric fermentation emission: farm level application   Order a copy of this article
    by Philippe Belmont Guerrón, María Hallo, Sergio Luján-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
     
  • An optimisation model of time-of-use pricing for ride-hailing platforms   Order a copy of this article
    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