InstanzenCLSP-RM

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.带产品回收的产能约束批量调度问题（capacitated lot-sizing problem with product recovery, CLSP-RM）在逆向物流领域具有重要研究价值，却素以复杂度极高著称，属于NP难问题。本研究提出两种技术以应对该挑战。第一种技术涉及构建可在广泛问题场景中消除产能约束的线性优化任务，可生成与最优解极为接近的近似解（模型A）。该方法具备良好的适应性，是一种高效且极具潜力的替代方案，且由于其通用性强，有望拓展至各类问题场景。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）的仿真方法解决该问题。该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、库存开支以及生产排序问题。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，共计约4000个测试实例。