Research on Hybrid Job Shop Scheduling Optimization Based on Genetic Algorithms and Simulated Annealing

In modern manufacturing systems and service industries, job scheduling problems are key to optimizing production processes, improving efficiency, and reducing costs. Particularly, the issue of minimizing the maximum processing time has attracted widespread attention. This paper introduces an innovative dual-population strategy and combines genetic algorithms with simulated annealing, aiming to efficiently solve job scheduling problems by leveraging the advantages of both. In this strategy, the main population is primarily driven by genetic algorithms, while the exploration population integrates the simulated annealing process, seeking a good balance between global search and local optimization. The dual-population approach not only expands the search space but also increases the diversity of solutions, thereby enhancing the possibility of finding solutions close to the global optimum. Through empirical validation, this paper's algorithm not only provides an effective new tool for solving complex scheduling problems but also offers useful references for the future application of intelligent algorithms in other fields.