Computers & Chemical Engineering ( IF 4.3 ) Pub Date : 2021-08-05 , DOI: 10.1016/j.compchemeng.2021.107472 Hêriş Golpîra 1 , Ahvan Javanmardan 1
This paper proposes a novel risk-based robust mixed-integer linear programming to design a decentralized closed-loop supply chain. The model is formulated as an uncertain bi-level multi-objective programming with multiple suppliers, manufacturers, and distributors, as the leader, and recovery, recycling, and disposal centers as the follower. A Scenario-based Conditional Value-at-Risk is employed to capture the demand uncertainty. The Karush-Kuhn-Tucker approach, constraint, and LP-metric are leveraged to deal with the complexity of the bi-level coordination, and the multi-objectivity of the leader and follower. The performance of the model is compared with the performance of the deterministic decentralized model and the corresponding multi-objective model designed for the centralized system in both the robust and deterministic modes. Results indicate better performance of robust approaches compared to deterministic approaches. The decentralized approach provides better performance for the cost-sensitive decision-maker, especially the optimistic one, and those who are sensitive to social parameters prefer the centralized approach.
中文翻译:
闭环供应链的分散决策系统:一种基于双层多目标风险的稳健优化方法
本文提出了一种新的基于风险的鲁棒混合整数线性规划来设计分散的闭环供应链。该模型被表述为以多个供应商、制造商和分销商为主导,回收、再循环和处置中心为跟随者的不确定双层多目标规划。采用基于情景的条件风险价值来捕捉需求不确定性。卡鲁什-库恩-塔克方法,约束和 LP 度量被用来处理双层协调的复杂性以及领导者和追随者的多目标性。将模型的性能与确定性分散模型和为集中式系统设计的相应多目标模型在鲁棒性和确定性模式下的性能进行比较。结果表明,与确定性方法相比,稳健方法具有更好的性能。分散式方法为对成本敏感的决策者提供了更好的性能,尤其是乐观的决策者,而那些对社会参数敏感的人更喜欢集中式方法。