基于 Prim-DMGA 算法的闭环供应链 网络鲁棒优化研究.

This paper proposed a robust peer-to-peer optimization method in the context of uncertain environments to address the uncertainty in closed-loop supply chain networks while considering demand uncertainty and facility disruption risk. This paper formulated a robust optimization model to minimize the total cost of the closed-loop supply chain network. Additionally, this paper proposed a Prim-DMGA that firstly identified high-quality initial solutions using the Prim algorithm and then optimized the routing and facility selection solutions in a two-layer adaptive GA feedback loop. The experimental results demonstrate that the Prim-DMGA generates better objective function values than the single-layer Prim-MGA and traditional GA with less computational time. Further, the results obtained from the algorithm are superior to those obtained by using the CPLEX software in solving large-scale scenarios. It concludes that the proposed robust optimization model effectively reduces the adverse effects caused by uncertainty and improves the robustness of closed-loop supply chain networks. Moreover, the Prim-DMGA generates better solutions with less computational time. [ABSTRACT FROM AUTHOR]