Use of genetic algorithms in multi-objective multi-project resource constrained project scheduling

Official URL: http://192.168.1.20/record=b1558928 (Table of Contents)

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 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 procedure is proposed as both post-processing as well as for new population generation. Additionally, different divergence applications are proposed and one of them, which is based on entropy measure is studied in depth. The performance of the algorithm and CPU times are reported. In addition, a new method for generating multi-project test instances is proposed and the performance of the algorithm is evaluated through test instances generated through this method of data generation. The results show that backward-forward pass procedure is successful to improve the solution quality.