Optimizing large-scale inventory networks: An iterative decomposition approach
本文提出迭代分解方法，通过分析局部解并选择合适算法求解子问题，高效优化大规模库存网络的安全库存配置，实验表明该方法在大规模复杂网络中表现更优。
Optimizing safety stock placement on a large-scale inventory network is challenging since it may involve a massive number of nodes and many shared materials. This article studies how to solve the large-scale guaranteed service model (GSM) to handle this problem. It presents a novel approach, called the iterative decomposition (ID) approach, to efficiently solve the large-scale GSM. The ID approach decomposes the problem iteratively by analyzing local solutions obtained from a non-convex optimization method (sequential linear programming) and selecting the appropriate algorithm to solve each subproblem based on its specific network structure. Our approach is evaluated against several methods from the literature using generated inventory networks. Our numerical experiments show that the ID approach performs better when the network size is large and the network structure is complex. We provide a Python library called InvNet, which provides the implementation for all methods considered in this article.