InstancesCLSP-RM (Paper: https://www.preprints.org/manuscript/202304.0242/v4)

The capacitated lot-sizing problem with product recovery (CLSP-RM) holds significant importance in reverse logistics but is notoriously complex (NP-hard). In this study, two techniques are introduced to confront this challenge. The first technique entails devising a linear optimization task that eliminates capacity limitations across a wide problem spectrum, yielding a remarkably accurate approximation of the optimal solution (Model A). This adaptable approach presents a potent alternative and holds potential for extension to diverse problem categories owing to its versatile nature. The second technique (Model B) employs a simulation methodology utilizing Halton’s uniform random numbers to address the issue. This randomized production search method sidesteps considerations of production costs, inventory expenditures, and production order when determining production batches . Here are the input data and solution of each instance solved with model A and model B respectively. There are about 4000 instances. (The test instances and solutions are available here: https://www.preprints.org/manuscript/202304.0242/v4 )带产品回收的能力约束批量问题（capacitated lot-sizing problem with product recovery, CLSP-RM）在逆向物流领域具有重要研究价值，但其本身属于公认的复杂NP难（NP-hard）问题。本研究提出两种技术以应对该挑战。第一种技术通过构建线性优化任务，可在广泛的问题场景中消除容量约束，能够高精度逼近最优解（模型A）。该方法具备良好的可扩展性，凭借其通用性，有望拓展至各类不同的问题范畴，是一种极具潜力的替代方案。第二种技术（模型B）采用基于哈尔顿均匀随机数（Halton’s uniform random numbers）的仿真方法来解决该问题。这种随机化生产搜索方法在确定生产批量时，无需考虑生产成本、库存成本以及生产订单等因素。以下为分别使用模型A与模型B求解的各测试实例的输入数据与求解结果。本数据集共包含约4000个测试实例（测试实例与求解结果可通过以下链接获取：https://www.preprints.org/manuscript/202304.0242/v4）