A MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION FOR FLOW SHOPS SCHEDULING PROBLEMS

Most research on flow shops scheduling problem focused on single objective problems, such as minimizing completion time, minimizing total flow time, and minimizing total tardiness. in order to solve NP-hard scheduling problems, numerous studies were conducted to develop meta-heuristics for obtaining the approximate optimal solution. However, empirical scheduling decisions should not only involve the deliberation of more than one objective at a time, but also try to prevent the conflict of two or more objectives. Therefore, the aim of this paper is to demonstrate the construction of Particle Swarm Optimization (PSO) to elaborate multi-objective flow shops scheduling problem. The original PSO is used to solve continuous optimization problems. Due to the discrete solution spaces of scheduling optimization problems, the authors modified the particle position representation, particle movement, and particle velocity in this study. The experiments were designed to find out the portfolio of parameters to better suit PSO for discrete scheduling problems. The modified PSO could be applied for solving various benchmark problems; moreover, the results demonstrated that the modified PSO performed better in searching quality and efficiency than traditional metaheuristics.