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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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    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