Introducing Learning Effects in Resource-constrained Project Scheduling

Learning effects assume that the efficiency of a resource increases with the duration of a task. Although these effects are commonly used in machine scheduling environments, they are rarely used in a project scheduling setting. In this paper, we study and model learning effects in a project scheduling environment and apply the model to the discrete time/resource trade-o_ scheduling problem (DTRTP), where each activity has a _fixed work content for which a set of execution modes (duration/resource requirement pairs) can be defined. Computational results emphasize the significant impact of learning effects on the project schedule, measure the margin of error made by ignoring learning and show that timely incorporation of learning effects can lead to significant makespan improvements.