摘要
针对预防性维修与工件到达时间的等效并行机调度问题,以最大完工时间的最小化为目标,设计了一种基于灾变机制的改进遗传算法。该算法采用随机与启发式混合方法生成初始种群,能以自适应交叉概率、变异概率以及灾变算子提高种群的多样性和算法的搜索能力,克服传统遗传算法的“早熟”问题。实验结果表明,与数学规划模型和传统遗传算法相比,该算法的求解效率有明显提高。
An improved genetic algorithm based on catastrophe mechanism is designed for the equivalent parallel scheduling problem considering preventive maintenance and job arrive time,aiming at minimizing the maximum completion time.The algorithm uses a mixture of random and heuristic methods to generate the initial population,which can improve the diversity of the population and the search ability of the algorithm with adaptive crossover probability,mutation probability and catastrophe operator,and overcome the“premature”problem of traditional genetic algorithms.The experimental results show that compared with the mathematical programming model and the traditional genetic algorithm,the efficiency of the algorithm is significantly improved.
作者
刘宇
吴杰程
钱晨红
潘厉冰
LIU Yu;WU Jiecheng;QIAN Chenhong;PAN Libing(School of Mechanical and Electrical Engineering,Wenzhou University,Wenzhou 325035,China)
出处
《成组技术与生产现代化》
2022年第3期39-46,共8页
Group Technology & Production Modernization
基金
国家级大学生创新创业训练计划资助项目(JWSC2019103)。
关键词
遗传算法
灾变算子
等效并行机调度
预防性维修
genetic algorithm
catastrophe operator
equivalent parallel machine scheduling
preventive maintenance
