Hybrid Multi-Objective PSO with Solution Diversity Extraction for job-shop scheduling management

The Multi-Objective Flexible Job-Shop Scheduling Problem (FJSP), which concerned with allocating limited resources to optimize some performance criteria, is difficult to find optimal scheduling solutions because of NP-hard complexity. In this paper, the particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA) is proposed to always produce feasible candidate solutions for the FJSP. Then a solution searching strategy called Solution Diversity Extraction is adopted to improve the Particle Swarm Optimization (PSO) to deal with the diversity in Pareto-optimal solutions. To test the performance of the proposed method, the experiments contain six representative benchmarks and to compare the proposed method with the published algorithms. The simulation results indicate the proposed method can find more wide range potential solutions, and outperform related methods.