Scenario-based evolutionary approach for robust RCPSP

Abstract

The present paper deals with the resource-constrained project scheduling problem with uncertain activity durations. Based on scenarios, we investigate two robust models, the min-max model which focuses on the minimization of the absolute robustness objective and the min-max regret model having the object to minimize the absolute regret. We propose an adaptive robust genetic approach with a sophisticated initial population and a Forward-Backward Improvement heuristic. The proposed algorithm is applied for the PSPLIB J30 data set with modified activity durations. Obtained results show the performance of the genetic algorithm combined with the improvement heuristic compared with the basic version. Different perturbation levels were tested to determine the corresponding performance degradation.