Dynamic search control-based particle swarm optimization for project scheduling problems

Many machinery manufacturings are categorized as multi-mode resource-constrained project scheduling problems which have attracted significant interest in recent years. It has been shown that such problems are non-deterministic polynomial-time-hard. Particle swarm optimization is one of the most commonly used metaheuristic. Multi-mode resource-constrained project scheduling problems comprise two sub-problems, namely, an activity operating priority and an activity operating mode sub-problems; hence, two particle swarm optimizations are used to solve these two sub-problems. In solving the activity priority sub-problem, a designed global guidance ratio is involved to control the particle’s search behavior. Restated, guiding a diversification search at the beginning stage and conducting an intensification search at latter stage are controlled by adjusting the global guidance ratio. The particle swarm optimization combined with the global guidance ratio mechanism is named global guidance ratio–particle swarm optimization herein. Meanwhile, a non-fixed global guidance ratio adjustment is also suggested to further enhance the search performance. Moreover, different communication topologies for balancing the convergence of using global and local topologies are also suggested in global guidance ratio–particle swarm optimization to further improve the search efficiency. The performance of the proposed global guidance ratio–particle swarm optimization scheme is evaluated by solving all the multi-mode resource-constrained project scheduling problem instances in Project Scheduling Problem Library. It is shown that the scheduling solutions are in good agreement with those presented in the literatures. Hence, the effectiveness of the proposed global guidance ratio–particle swarm optimization scheme is confirmed