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
周伟 1孙瑜 1李西兴 1王林琳1
作者信息
- 1. 湖北工业大学机械工程学院现代制造质量工程湖北省重点实验室,湖北武汉 430068
- 折叠
摘要
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.
Abstract
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.
关键词
柔性作业车间调度/加工成本/遗传算法/变邻域搜索/混合算法/动态概率/优化Key words
flexible job-shop scheduling problem/processing cost/genetic algorithm/variable neighborhood search/hybrid algo-rithm/dynamic probability/optimization引用本文复制引用
基金项目
国家自然科学基金项目(51805152)
湖北工业大学绿色工业引领计划基金项目(XJ2021005001)
湖北工业大学博士科研启动基金项目(BSQD2019010)
出版年
2024
NETL
NSTL
万方数据