An Efficient Particle Swarm Optimizer with Application to Man-Day Project Scheduling Problems

The multi-mode resource-constrained project scheduling problem (MRCPSP) has been identified to be an NP-hard problem. MRCPSP includes solving two sub-problems: mode assignment and activity priority determination. Hence, two particle swarm optimizations (PSOs) are applied to each sub-problem. A constriction PSO is proposed for the activity priority determination and a discrete PSO is for the mode assignment. Least total resource usage (LTRU) and minimum slack (MSLK) heuristics ensure better initial solutions. Hence, to generate a collection of diverse initial solutions and thereby enhance the PSO efficiency, a best heuristic rate (HR) is suggested. To verify the performance, all instances of the MRCPSP benchmarks in PSPLIB were evaluated. The experimental results reveal that the proposed algorithm has high performance and is efficient for solving the MRCPSP problems. Therefore, a real-world man-day project scheduling problem (MDPSP) was evaluated and the results demonstrate that MDPSP can be solved successfully.