基于改进遗传算法的柔性作业车间调度问题研究

Research on Flexible Job Shop Scheduling Problem Based on Improved Genetic Algorithm

为了应对当下愈加复杂的实际生产情况,针对柔性作业车间调度问题(FJSP),以最大完工时间最小为目标,提出一种更加简洁有效的优化算法用以求解该问题。针对遗传算法(GA)局部搜索能力较差等问题,结合教与学算法(TLBO)优异的局部搜索能力,在交叉阶段引入教与学交叉以提高算法局部搜索能力;考虑到进化时期不同对于交叉等操作的需求不同,引入自适应策略提高算法的全局搜索能力。先将改进后的TGA算法与传统GA算法对比以验证算法的有效性,再与改进的蝙蝠算法(HGBA)对比验证算法的简洁性,通过结果图的对比得出本文改进算法对于求解FJSP问题的简洁有效性。

In order to deal with the increasingly complex actual production situation,this paper aims to propose a more concise and effective optimization algorithm to solve the flexible job shop scheduling problem(FJSP)with the goal of minimizing the maximum completion time.In view of the poor local search ability of genetic algorithm(GA),combined with the excellent local search ability of teaching-learning-based optimization algorithm(TLBO),teaching and learning crossover is introduced in the crossover phase to improve the local search ability of the algorithm.Considering the different requirements for crossover and other operations in different evolutionary periods,an adaptive strategy is introduced to improve the global search ability of the algorithm.In this paper,the improved TGA algorithm is compared with the traditional GA algorithm to verify the effectiveness of the algorithm,and then compared with the improved bat algorithm(HGBA)to verify the simplicity of the algorithm.Through the comparison of the result graph,it is concluded that the improved algorithm in this paper is concise and effective for solving FJSP problems.