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Designing a data-driven leagile sustainable closed-loop supply chain network

机译:设计数据驱动的Ineginile可持续闭环供应链网络

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摘要

Nowadays, there is a great deal of interest in applying sustainability concepts for logistics and supply chain management. This paper proposes a new multi objective model in the area of closed loop supply chain problem integrated with lot sizing by considering lean, agility and sustainability factors simultaneously. In this regard, responsiveness, environmental, social and economic aspects are regarded in the model in addition to the capacity and service-level constraints. Most importantly, strategic and operational backup decisions are developed to increase the resiliency of the system against disruption of the facilities and routes simultaneously. In the following, a new hybrid metaheuristic algorithm comprised a parallel Multi-Objective Particle Swarm Optimization (PMOPSO) algorithm and a multi objective social engineering optimizer (MOSEO) is developed to deal with large size problems efficiency. To ensure about the effectiveness of the proposed hybrid algorithm, the results of this algorithm are compared with a Non-dominated Sorting Genetic Algorithm (NSGA-II).
机译:如今,对应用物流和供应链管理的可持续发展概念有很大兴趣。本文提出了一种新的多目标模型,通过考虑瘦,敏捷和可持续性因子,在封闭回路供应链问题中集成的封闭环路供应链问题。在这方面,除了能力和服务水平限制之外,该模型还认为响应性,环境,社会和经济方面。最重要的是,制定了战略和运营备份决策,以增加系统同时破坏设施和路线的弹性。在下文中,新的混合成群质算法包括并行多目标粒子群优化(PMOPSO)算法和多目标社会工程优化器(MOSEO),以处理大尺寸的问题效率。为了确保提出的混合算法的有效性,将该算法的结果与非主导的分类遗传算法(NSGA-II)进行了比较。

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