Research on Improved Genetic Algorithm Based on Multi-Objective Optimization for Flexible Shop Scheduling Problem

This paper addresses the Flexible Job Shop Scheduling Problem (FJSP) in manufacturing by proposing an enhanced genetic algorithm based on multi-objective optimization. As an NP-hard problem, FJSP involves optimizing multiple conflicting objectives, including minimizing makespan, reducing machine load, and improving resource utilization. With the shift toward intelligent systems in manufacturing, efficient workshop scheduling has become crucial for enhancing production efficiency. The proposed algorithm integrates multi-objective optimization techniques to overcome the limitations of traditional methods, maintaining robust global search capabilities to avoid premature convergence and improve convergence speed. By incorporating Pareto multi-objective optimization and an external memory mechanism, the algorithm not only enhances the quality of initial solutions but also increases the survival probability of high-quality individuals during the evolutionary process, significantly improving overall optimization performance. Experimental results demonstrate the algorithm's effectiveness in solving complex FJSP instances.