Abstract This paper addresses a multi-skilled extension of the resource-constrained project scheduling problem (RCPSP). Although a handful of papers dealt with the multi-skilled RCPSP (MSRCPSP), little to no attention is given to the ideal levels of skills for multi-skilled resources. In this paper, skills are measured along two dimensions known as breadth and depth. In a project environment, the breadth of a resource is perceived as the amount of skills an employee masters. The depth of a skill is the efficiency level at which work can be performed by a resource that masters that skill. The MSRCPSP with breadth and depth consists of scheduling activities with skill requirements and assigning multi-skilled resources to those activities. To be able to efficiently solve the MSRCPSP, a genetic algorithm is developed. Using the created activity schedules and resources assignments, the best workforce characteristics are analysed. Key aspects in this analysis are the breadth and depth. The problem-specific procedure combines a new representation, a new crossover and tailor-made local searches. Computational experiments measure the impact of different multi-skilled resources and their efficiency levels on the makespan of the project.

中文翻译：

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一种具有广度和深度技能的资源受限项目调度新算法

摘要 本文解决了资源受限项目调度问题 (RCPSP) 的多技能扩展。尽管少数论文涉及多技能 RCPSP (MSRCPSP)，但很少或根本没有关注多技能资源的理想技能水平。在本文中，技能是从广度和深度两个维度来衡量的。在项目环境中，资源的广度被视为员工掌握的技能数量。技能的深度是指掌握该技能的资源可以执行工作的效率水平。具有广度和深度的 MSRCPSP 包括安排具有技能要求的活动并将多技能资源分配给这些活动。为了能够有效地解决MSRCPSP，开发了一种遗传算法。使用创建的活动计划和资源分配，分析最佳劳动力特征。该分析的关键方面是广度和深度。特定于问题的程序结合了新的表示、新的交叉和量身定制的本地搜索。计算实验测量了不同的多技能资源及其效率水平对项目完工时间的影响。