This paper studies and analyses the multi-skilled resource-constrained project scheduling problem (MSRCPSP). We present a new classification scheme based on an existing classification scheme for project scheduling problems. This allows researchers to classify all multi-skilled project scheduling problems and its extensions. Furthermore, we propose a new data generation procedure for the MSRCPSP and introduce multiple artificial datasets for varying research purposes. The new datasets are generated based on new multi-skilled resource parameters and are compared to existing benchmark datasets in the literature. A set of 7 empirical multi-skilled project instances from software and railway construction companies are collected in order to validate the quality of the artificial datasets. Solutions are obtained through a genetic algorithm and by solving a mixed-integer linear programming formulation with CPLEX 12.6. The hardness of the multi-skilled project instances is investigated in the computational experiments. An experimental analysis studies the impact of skill availability, workforce size and multi-skilling on the makespan of the project.

本文研究分析了多技能资源约束项目调度问题（MSRCPSP）。我们基于现有的项目调度问题分类方案提出了一种新的分类方案。这允许研究人员对所有多技能项目调度问题及其扩展进行分类。此外，我们为 MSRCPSP 提出了一种新的数据生成程序，并为不同的研究目的引入了多个人工数据集。新数据集是根据新的多技能资源参数生成的，并与文献中现有的基准数据集进行比较。收集了一组来自软件和铁路建设公司的 7 个经验性多技能项目实例，以验证人工数据集的质量。解决方案是通过遗传算法和使用 CPLEX 12.6 求解混合整数线性规划公式来获得的。在计算实验中研究了多技能项目实例的硬度。一项实验分析研究了技能可用性、劳动力规模和多技能对项目完成时间的影响。