阻塞混合流水车间调度优化研究  

Research on Blocking Hybrid Flow Shop Scheduling Optimization

作　　者：轩华[1] 王晶 李冰[1] 王薛苑[1] XUAN Hua;WANG Jing;LI Bing;WANG Xue-yuan(School of Management Engineering,Zhengzhou University,Zhengzhou 450001,China)

机构地区：[1]郑州大学管理工程学院,河南郑州450001

出　　处：《控制工程》2020年第8期1346-1350,共5页Control Engineering of China

基　　金：国家自然科学基金资助项目(U1804151,U1604150);教育部人文社会科学研究基金项目(15YJC630148);河南省科技攻关计划项目(202102310310)。

摘　　要：研究了以最小化最大完工时间为目标的阻塞混合流水车间调度问题,假定工件随机到达生产系统,建立混合整数规划模型,提出一种嵌入局域搜索的自适应遗传算法得到该问题的近优解。为克服传统遗传算法局域搜索能力较差,迭代后期进化能力较低的不足,改变传统遗传算法中交叉和变异概率固定不变的设定,设计随算法进程而自动调节的自适应概率,进而在迭代后期嵌入LS来改善解的质量。通过从小到大规模的仿真实验,对比遗传算法与嵌入局域搜索的自适应遗传算法的性能,结果说明了所提出算法的有效性。Blocking hybrid flow shop scheduling problem is studied with the objective of minimizing maximum completion time.Jobs are assumed to reach the production system randomly.A mixed integer programming model is then formulated and a local search based adaptive genetic algorithm is proposed to obtain near-optimal solutions.To avoid falling into local minima and poor evolutionary ability at late iterations,the setting on fixed crossover and mutation probability of traditional genetic algorithm is changed and adaptive probability is designed which can adjust automatically with the algorithm process.Further local search is introduced at late iterations so as to improve solution quality.Simulation experiments on small to large sized problems show the efficiency of the proposed algorithm by comparing the performance of genetic algorithm and local search based adaptive genetic algorithm.

关 键 词：阻塞混合流水车间调度 工件释放时间 自适应遗传参数 局域搜索 自适应遗传算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]