混合遗传变邻域搜索算法求解柔性车间调度问题

Hybrid genetic variant neighborhood search algorithm for flexible job-shop scheduling problem

针对考虑生产成本的柔性作业车间调度问题(flow job shop scheduling problem, FJSP),以完工时间与加工成本为优化指标,提出一种求解FJSP的混合遗传变邻域搜索算法。根据个体适应度对种群分割,结合自适应交叉概率改进子代种群产生方式;设计两种邻域结构增强算法的局部搜索能力;提出一种基于动态交叉变异概率的优化算法流程提高求解效率。运用提出的算法求解基准实例与实际问题测试,验证了算法的有效性。

Aiming at the flow job shop scheduling problem(FJSP)considering production cost and taking completion time and processing cost as optimization indexes,a hybrid genetic variable neighborhood search algorithm was proposed to solve FJSP.The population was segmented according to individual fitness and the generation method of progeny population was improved by combining adaptive crossover probability.The local search capabilities of two neighborhood structure enhancement algorithms were designed.An optimization algorithm flow based on dynamic cross mutation probability was proposed to improve the solving efficiency.The effectiveness of the proposed algorithm was verified by solving benchmark examples and practical problems.