The Optimization of Multi-objective FJSP Based on the Hybrid Algorithm

Abstract The flexible job-shop scheduling problem (FJSP) is a critical model in manufacturing systems that assigns operations from different jobs to various machines. However, optimizing multiple targets during the production process always necessary. While non-dominated sorting genetic algorithm (NSGA-II) an effective method solve multi-objective FJSP, it can have main drawbacks of converging too early and falling into local optimization. To address these issues, this research proposes hybrid combines NSGA-II simulated annealing using pareto-domination based acceptance criterion (PDMOSA). PDMOSA has powerful search performance overcome limitations NSGA-II. also includes original modification methods such as deletion criterion, duplicated solution deletion, new individual adding. Additionally, plug-in decoding introduced. proposed compared with several improved ways on experiments. results demonstrate better than others FJSP.