A genetic algorithm application for multi-objective multi-project resource constrained project scheduling problem

Resource Constrained Project Scheduling Problem (RCPSP) has been studied extensively by researchers by considering limited renewable and non-renewable resources. Several exact and heuristic methods have been proposed. Some important extensions of RCPSP such as multi-mode RCPSP, multi-objective RCPSP and multi-project RCPSP have also been focused. In this study, we consider multi-project and multi-objective resource constrained project scheduling problem. As a solution method, non-dominated sorting genetic algorithm (NSGA-II) is adopted. By experimenting with different crossover and parent selection mechanisms, a detailed fine-tuning process is conducted, in which response surface optimization method is employed. In order to improve the solution quality, backward-forward pass (BFP) procedure is proposed as both post-processing as well as for new population generation. The performance of the algorithm and CPU times are reported. The results show that backward-forward pass procedure is successful to improve the solution quality.