Abstract Today, due to the increasing environmental hazards and governmental regulations, as well as the limitation of sources of production, researchers have paid special attention to the design of closed-loop green supply chain networks. The closed-loop supply chain networks (CLSCN) include the returns processes and the producers aim to capturing additional value considering further integration of all supply chain activities. Therefore, all return processes need to be optimized as well as considering environmental impacts leading to form a closed-loop green supply chain network (CLGSCN). For decision making purposes, operational and tactical decision making levels are integrated to configure a coordinated supply chain network aiming to maximize profit while keeping environmental-friendly policies. The case is more sophisticated in melting industries where the collection and categorization in return process and different environmental challenges should be considered at the same time. Thus, in this paper, a CLGSCN of a melting industry is modeled with respect to environmental hazards to optimiza overall profits. Since real-world demand in melting industry under study is uncertain, the robust optimization has been employed, and while the optimization of the proposed mathematical model is time consuming, an improved version of the genetic algorithm has been implemented as a solution method. This study has been carried out at Melting Imen Tabarestan (MIT) company in Iran. The proposed model along with the solution method are investigated in the case study. The results imply the effectiveness and applicability of the model and provide tactical considerations for the managers and practitioners.

中文翻译：

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不确定性下闭环绿色供应链的鲁棒优化和改进遗传算法：熔炼行业案例研究

摘要 如今，由于环境危害和政府法规的日益增多，以及生产来源的限制，研究人员特别关注闭环绿色供应链网络的设计。闭环供应链网络 (CLSCN) 包括退货流程，考虑到所有供应链活动的进一步整合，生产商旨在获取额外价值。因此，所有退货流程都需要优化，同时考虑环境影响，从而形成闭环的绿色供应链网络（CLGSCN）。出于决策目的，运营和战术决策层被整合以配置一个协调的供应链网络，旨在在保持环境友好政策的同时实现利润最大化。熔炼行业的情况更为复杂，需要同时考虑退货过程中的收集和分类以及不同的环境挑战。因此，在本文中，针对环境危害对熔炼行业的 CLGSCN 进行建模，以优化整体利润。由于所研究的熔炼行业的实际需求不确定，因此采用了鲁棒优化，虽然所提出的数学模型的优化很耗时，但已经实施了遗传算法的改进版本作为求解方法。这项研究是在伊朗的 Melting Imen Tabarestan (MIT) 公司进行的。在案例研究中研究了所提出的模型以及求解方法。