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    Designing a two-stage model for a sustainable closed-loop electric vehicle battery supply chain network: A scenario-based stochastic programming approach
    (Pergamon-Elsevier Science Ltd, 2024) Saeedi, Mehran; Parhazeh, Sina; Tavakkoli-Moghaddam, Reza; Khalili-Fard, Alireza
    Transportation is a fundamental requirement of modern life. Vehicles powered by fossil fuels are highly polluting. This study develops a two-stage stochastic programming model to establish a sustainable closed-loop supply chain for Electric Vehicle (EV) batteries. The model considers economic, environmental, and social criteria, including cost, energy consumption, carbon emissions, and job creation. The epsilon-constraint method and three multi-objective meta-heuristic algorithms are utilized to solve problems. Implementing this model in a case study of an EV battery supply chain aids managerial decision-making for optimal center establishment, flow determination, and inventory setting. Finally, essential parameters are analyzed, and several important managerial insights are prepared. The results suggest that investing in used battery collection significantly reduces costs and carbon emissions.
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    A roommate problem and room allocation in dormitories using mathematical modeling and multi-attribute decision-making techniques
    (Emerald Group Publishing Ltd, 2024) Khalili-Fard, Alireza; Tavakkoli-Moghaddam, Reza; Abdali, Nasser; Alipour-Vaezi, Mohammad; Bozorgi-Amiri, Ali
    PurposeIn recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.Design/methodology/approachIn this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.FindingsThe results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method's applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.Originality/valueThis novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.