Backward-Forward Hybrid Genetic Algorithm for Resource-Constrained Multiproject Scheduling Problem

Abstract

Despite the fact that companies manage multiple projects simultaneously, most research on resource-constrained project scheduling has focused on single projects. This paper presents a backward-forward hybrid genetic algorithm (BFHGA) for optimal scheduling of a resource-constrained multiproject scheduling problem (RCMPSP). The new approach combines complementary strengths of the backward-forward scheduling method, genetic algorithms, and simulated annealing. BFHGA was tested on four single-project case examples, one portfolio case example, one real portfolio, and 26 test portfolio instances. The proposed algorithm obtained the best solution for all of the single-project case examples, and outperformed five state-of-the-art meta-heuristics and five popular heuristics for the resource-constrained multiproject scheduling problems. The computational results show that the BFHGA is a fast and effective algorithm for scheduling multiple projects with common limited resources. The performance gap between the BFHGA and popular heuristics reveals the potential for improving the existing heuristics for the RCMPSP.