Particle Swarm Optimization for Parallel Machine Scheduling Problem with Machine Eligibility Constraints

Particle swarm optimization is a popular global optimization technology in continuous and discrete optimization fields in recent years. This paper presents a particle swarm optimization-based scheduling algorithm for large-scale parallel machine scheduling problem with machine eligibility constraints and the objective of minimizing the total weighted tardiness. In the proposed method, we first design a Machine assignment heuristic (MAH) which is used to assign a processing machine of each job based on the dynamic load-balancing mechanism, and then we propose a particle swarm optimization algorithm to optimize the sequence of all jobs in which MAH is used in the decoding process of each particle, also, the mechanisms of particle-moving and velocity-updating are devised based on the problem characteristics. Numerical computational results show that the proposed algorithm is effective for large-scale parallel machine scheduling problems with machine eligibility constraints.