Hybrid particle swarm optimization for preemptive resource-constrained project scheduling

In this paper a hybrid particle swarm optimization procedure is proposed to solve the preemptive resource-constrained project scheduling problem in which a maximum of one interruption per activity is allowed. Four types of particle representations are designed and two schedule generation schemes are adopted to decode the particle representations. Particle-updating mechanisms based on the peak crossover operator are designed for all particle representations. Computational experiments have been carried out on standard project scheduling problem sets. Analysis of the computational results has confirmed that introduction of preemption helps to reduce project duration and the proposed particle swarm optimization procedures are effective for preemptive resource-constrained project scheduling.