Gehring Marco, Volk Rebekka, Schultmann Frank
Karlsruhe Institute of Technology, Institute for Industrial Production (IIP), Hertzstr. 16, 76187 Karlsruhe, Germany.
Data Brief. 2023 May 29;48:109279. doi: 10.1016/j.dib.2023.109279. eCollection 2023 Jun.
This data article describes an instance dataset motivated by the problem of scheduling a project with diverging material flows. Such material flows are released during the execution of the project and are subject to limited processing and storage capacities. Typical examples are nuclear dismantling or other deconstruction/demolition projects, where large amounts of material must be classified, scanned for hazardousness, and processed accordingly. The problem setting is mathematically described as a resource-constrained project scheduling problem with cumulative resources (RCPSP/c). The RCPSP/c deals with finding a project schedule with minimal makespan that satisfies temporal, renewable resource, and cumulative resource constraints. In total, the dataset comprises 192 artificially generated instances that are suitable for testing models and solution methods. In addition, we provide our best found solution for each instance and different modeling variants (e.g., for two types of objective functions). These solutions were computed by heuristic solution methods. The dataset serves as a benchmark for researchers evaluating the performance of solution methods for the RCPSP/c or the more general problem class with resources that can be produced and consumed.
本文描述了一个实例数据集，该数据集源于具有不同物料流的项目调度问题。此类物料流在项目执行过程中释放，并受到有限的加工和存储能力的限制。典型的例子是核拆除或其他拆除/拆卸项目，其中大量材料必须进行分类、扫描是否存在危险性并进行相应处理。该问题设置在数学上被描述为具有累积资源的资源受限项目调度问题（RCPSP/c）。RCPSP/c涉及找到一个具有最小完工时间的项目进度计划，该计划满足时间、可再生资源和累积资源约束。该数据集总共包含192个人工生成的实例，适用于测试模型和求解方法。此外，我们为每个实例和不同的建模变体（例如，针对两种类型的目标函数）提供了我们找到的最佳解决方案。这些解决方案是通过启发式求解方法计算得出的。该数据集作为研究人员评估RCPSP/c或具有可生产和消耗资源的更一般问题类别的求解方法性能的基准。
