Particle swarm optimization-based schemes for resource-constrained project scheduling

Abstract

Particle swarm optimization (PSO) performed through particle flying along the trajectory that will be continuously updated is based to develop a solution-solving scheme for the resource-constrained project scheduling problem (RCPSP). The potential solution to the RCPSP in view of minimizing project duration is represented by the multidimensional particle, where two solution representations, i.e., priority-based representation and permutation-based representation, are respectively considered. The frameworks of the PSOs for the RCPSP according to the two solution representations are developed. Experimental analyses are presented to investigate the performance of the proposed PSO-based methodology, including comparison of the two representations and comparison with other approaches for the RCPSP. The study aims at providing an alternative means for the RCPSP by utilizing the features of PSO, such as particle-updating mechanism that may benefit from the searching experience of one particle itself or the best of all particles in the swarm.