Hybrid Taguchi-Based Particle Swarm Optimization for Flowshop Scheduling Problem

A hybrid Taguchi-based particle swarm optimization (HTPSO) method is developed for solving multi-objective flowshop scheduling problems (FSPs). Search performance is improved using Taguchi-based crossover to avoid scheduling conflicts. Instead of the conventional approach to selecting dynamic weights randomly, which ignores very small weight values for the objective, a fuzzy inference system is used. A numerical example is given to demonstrate the application of the proposed HTPSO and its good performance. The numerical results show that the HTPSO effectively enhances particle swarm optimization. The improvement achieved by the HTPSO also exceeds that obtained by existing methods for finding Pareto optimum solutions for FSPs. Therefore, the proposed HTPSO method effectively solves multi-objective FSPs.