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    A data-driven hybrid scenario-based robust optimization method for relief logistics network design
    (Elsevier Ltd., 2025) Amin Amani, Mohammad; Asumadu Sarkodie, Samuel; Sheu, Jiuh Biing; Mahdi Nasiri, Mohammad; Tavakkoli Moghaddam, Reza
    The incorporation of artificial intelligence (AI) and robust optimization methods for the planning and design of relief logistics networks under relief demand–supply uncertainty appears promising for intelligent disaster management (IDM). This research proposes a data-driven hybrid scenario-based robust (SBR) method for a mixed integer second-order cone programming (MISOCP) model that integrates machine learning with a hybrid robust optimization approach to address the above issue. A machine learning technique is utilized to cluster the casualties based on location coordinates and injury severity score. Moreover, the hybrid SBR optimization method and robust optimization based on the uncertainty sets technique are utilized to cope with uncertain parameters such as the probability of facility disruption, the number of wounded individuals, transportation time, and relief demand. Additionally, the epsilon-constraint technique is applied to seek the solution for the bi-objective model. Focusing on a real case (the Kermanshah disaster), our analytical results have demonstrated not only the validity but also the relative merits of the proposed methodology against typical stochastic and robust optimization approaches. Besides, the proposed method shows all casualties can be efficiently transported to receive medical services at a fair cost, which is crucial for disaster management. © 2024 Elsevier Ltd
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    A hybrid simheuristic algorithm for solving bi-objective stochastic flexible job shop scheduling problems
    (Elsevier Inc., 2024) Nessari, Saman; Tavakkoli Moghaddam, Reza; Bakhshi Khaniki, Hessam; Bozorgi Amiri, Ali
    The flexible job shop scheduling problem (FJSSP) is a complex optimization challenge that plays a crucial role in enhancing productivity and efficiency in modern manufacturing systems, aimed at optimizing the allocation of jobs to a variable set of machines. This paper introduces an algorithm to tackle the FJSSP by minimizing makespan and total weighted earliness and tardiness under uncertainty. This hybrid algorithm effectively addresses the complexities of stochastic multi-objective optimization by integrating the equilibrium optimizer (EO) as an initial solutions generator, Non-dominated sorting genetic algorithm II (NSGA-II), and simulation techniques. The algorithm's effectiveness is validated by showcasing specific instances and delivering decision results for optimal scheduling across varying levels of uncertainty. Results reveal the algorithm's consistent superiority in managing the complexities of stochastic parameters across various problem scales, achieving lower makespan and improved Pareto front quality compared to existing methods. Particularly notable is the algorithm's faster convergence and robust performance, as validated by the statistical Wilcoxon test, which confirms its reliability and efficacy in handling dynamic scheduling situations. These findings underscore the algorithm's potential in providing flexible, robust solutions. The proposed algorithm's unique balance of exploitative and explorative capabilities within a simulation framework enables effective handling of uncertainty in the FJSSP, offering flexibility and customization that is adaptable to various scheduling environments. © 2024 The Author(s)
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    A sustainable vaccine supply-production-distribution network with heterologous and homologous vaccination strategies: Bi-objective optimization
    (Elsevier Ltd., 2025) Jahed, Ali; Hadji Molana, Seyyed Mohammad; Tavakkoli Moghaddam, Reza
    Heterologous and homologous Coronavirus Disease 2019 (COVID-19) vaccination against Severe Acute Respiratory Syndrome (SARS)-CoV-2 are robust and proactively adaptable strategies. However, there is still a lack of appropriate mathematical models for integrating vaccination strategies into the vaccine supply chain network. This study develops a supply-production-distribution-inventory-allocation problem in the Sustainable Vaccine Supply-Production-Distribution Network (SVSPDN) to fill this gap for the first time. The outstanding novelties of this research are prioritizing vaccines and sequencing injection doses to increase vaccination effectiveness. In addition, the remarkable new contribution of the proposed mathematical model is the design of new bi-objective, multi-dose, multi-level, and multi-period to ensure the sustainability performance of the entire network. This aim is achievable by minimizing the cost of supplying, producing, and distributing vaccines and fulfilling social goals by maximizing vaccination effectiveness. Also, a scenario-based robust stochastic optimization approach is presented to handle uncertainties. Since the SVSPDN design is an NP-hard problem, to solve the proposed mathematical model, three Pareto-based evolutionary algorithms, including Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Gray Wolf Optimizer (MOGWO), are applied. The Taguchi design method is applied to tuning the parameters due to the sensitivity of meta-heuristic algorithms to input parameters. Then, a comparison is performed using four assessment metrics, including the Number of Pareto Solutions (NPS), Diversification Matrix (DM), Mean Ideal Distance (MID), Spread of Non-Dominance Solutions (SNS), and Computation Time (CT). The results reveal that the NSGA-II and MOGWO algorithms have performances that are very close to each other. However, MOGWO performs better in tackling the problem and is superior to the NSGA-II and MOPSO regarding assessment metrics and computation time. A case study of Iran is investigated to indicate the efficiency and applicability of the proposed model. Finally, sensitivity analyses, managerial insights, and practical implications are discussed. © 2024 Elsevier Ltd
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    Designing a Model for Optimizing Operation of Production Processes in Industry 4.0
    (Materials and Energy Research Center, 2024) Lajevardi M.; Nikbakht M.; Boyer O.; Tavakkoli Moghaddam, Reza
    Industry 4.0 represents a shift from centralized to decentralized production, involving cyber-physical systems. This transformation aims to reduce costs and wastes by implementing timely preventive measures. Our study proposes an optimized model for production processes in Industry 4.0's cellular manufacturing systems. We employ a combination of failure mode and effects analysis (FMEA), prospect theory (PT), and Shannon entropy for this purpose. The critical failure modes are identified through a comparison with reference points. Shannon entropy is used to determine the importance of each risk factor in the FMEA method. By calculating the utility value through the prospecting method, the critical failure modes are prioritized. Specifically, FM7 and FM14 are assigned the first and second priority, respectively. To validate our approach, we evaluate its effectiveness in a real world scenario an auto parts manufacturing factory. This proposed framework empowers managers to enhance risk analysis, control factors and the impact of failure modes, and elevate safety and reliability levels in production systems. ©2024 The author(s).
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    Designing an integrated sustainable-resilient mix-and-match vaccine supply chain network
    (Springer, 2024) Jahed, Ali; Molana, Seyyed Mohammad Hadji; Tavakkoli Moghaddam, Reza; Valizadeh, Vahideh
    Vaccination is the most effective strategy for battling infectious diseases, breaking the disease transmission chain, and achieving herd immunity. Implementing vaccination for the whole population requires an integrated vaccine supply chain network that considers sustainability and resiliency in the network. For this purpose, in this research, a location-allocation-inventory-distribution problem in the sustainable and resilient vaccine supply chain network, considering mix-and-match vaccine regimens against SARS-CoV-2, is designed. The mix-and-match-based vaccination to reach robust immunization, increase vaccination effectiveness, and more resilience to cope with shortages is applied. In addition, three pillars of sustainability, to minimize distribution network costs, vaccine disposal impact, and greenhouse gas emissions, in terms of economic and environmental, and maximizing job creation, demand satisfaction, and vaccination effectiveness to ensure social sustainability, are developed. Also, scenario-based optimization is presented to meet the inevitable disruptions and breakdowns, such as the supply capacity of suppliers and uncertain amounts of vaccine demand, which depends on the previous type of vaccine injected, and robust stochastic programming is used to handle uncertainties. To solve the proposed model, efficient meta-heuristic algorithms, including the genetic algorithm (GA) and variable neighborhood search (VNS), are applied. In addition, a new hybrid algorithm called H-GAVNS based on the GA and VNS is developed in this research to discover near-optimal results. Finally, a case study of the COVID-19 vaccine in Iran’s environment is presented to confirm the accuracy of the presented model. The outcomes show that uncertainties in the real world and sustainability and resiliency aspects are well managed and responded to by the designed model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
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    Energy-resilient closed-loop supply chain design managed by the 3PL provider: A pick-up strategy and data envelopment analysis
    (Elsevier B.V., 2025) Moghadaspoor, Beheshteh; Tavakkoli Moghaddam, Reza; Bozorgi Amiri, Ali; Allahviranloo, Tofigh
    Population growth and the development of transportation networks have caused the world to face a larger volume of scrap tires, which can cause critical environmental challenges if they are not properly disposed of after being ultimately used. Thus, implementing appropriate recovery practices has developed. The existing challenges in the forward and reverse integration flow motivate leaders to submit a third-party logistics service provider (3PL) as an appropriate option for outsourcing activities. As a result, an inventive closed-loop supply chain (CLSC) network is necessary. A multiple objective, product, and period mathematical model is proposed to develop the CLSC under 3PL management in the tire industry. The data envelopment analysis (DEA) method is applied to choose a better set of manufacturers to coordinate with 3PL. The motivating pricing approach is also considered for appropriate recovery practices, and resiliency was investigated against disruption at crucial levels. This model aims to minimize the costs of diverse processes over scrap products and energy consumption and reach a sufficient level of responsiveness to customers. For solving the multi-objective model, the augmented ε-constraint (AUGMECON2) method leads to Pareto-optimal solutions. The results show that 3PLs improve the supply chain (SC) procedure and increase the responsiveness to customer demand. Also, by planning to increase product recycling, it is possible to save money when purchasing raw materials from suppliers. © 2024