基于混合遗传算法的车间生产调度问题研究

摘要

The aim of solving Job Shop Scheduling Problem is not only to reduce makespan but also to improve the efficiency of production and reduce the operating cost. Now most of the state - owned manufacturing enterprises use artificial scheduling in job - shop scheduling. Workshops mainly depend on the scheduling experiences, so the scheduling efficiency is not high and there are more mistakes. This paper proposes a hybrid genetic algorithm to solve Job -Shop Scheduling Problem through combining the genetic algorithm with simulated annealing. A new encoding method is presented for this hybrid algorithm, and the corresponding decoding method is established. The genetic algorithm makes sure that the solution is global optimization and the result of simulation shows the hybrid algorithm is feasible and practicable, and it can effectively improve the efficiency of searching and the convergence.%解决车间生产调度问题的日的不仅仅足为了缩短生产周期,更重要的是为了提高生产效率,降低生产成本.现大部分国有制造企业在车间生产过程中采用的是人工调度,调度时主要依赖于调度经验,调度效率不高且易出错.将遗传算法和模拟退火算法相结合,提出丁解决车间调度问题的混合遗传算法,并给出了一种编码方法以及建立了相应的解码规则.遗传算法的引入保证了解的全局最优性,仿真后表明了该混合算法的可行性和有效性,且能够有效地提商搜索效率,改进了收敛性能.