Learning Effect and Compound Activities in High Multiplicity RCPSP: Application to Satellite Production

Abstract

Duc Anh, from Onera, the French aeronautics, space and defense research lab, tells about the High Multiplicity Resource-Constrained Project Scheduling Problem (HMRCPSP), in which multiple projects are performed iteratively while sharing limited resources. We extend this problem by integrating the learning effect, which makes the duration of some activities decrease when they are repeated. Learning effect can be represented by any decreasing function,allowing us to get flexibility in modeling various scenarios. Additionally, we take composition of activities into consideration for reasoning about precedence and resources in a more abstract way. A Constraint Programming model is proposed for this richer problem, including a symmetrybreaking technique applied to some activities. We also present a heuristic-based search strategy. The effectiveness of these solving approaches is evaluated through an experimentation conducted on data concerning real-world satellite assembly lines, as well as on some adapted literature benchmarks. Obtained results demonstrate that our methods serve as robust baselines for addressing this novel problem (denoted by HM-RCPSP/L-C)