超大规模车间作业调度优化方法研究与仿真  

Large Scale Workshop Scheduling Problem Optimization Method Research and Simulation

作　　者：刘军[1] 陈瑞生[1] 王晔楠[1] 

机构地区：[1]兰州理工大学机电工程学院,甘肃兰州730000

出　　处：《计算机仿真》2013年第2期291-294,共4页Computer Simulation

基　　金：国家自然基金(51265032);长江学者和创新团队发展计划

摘　　要：研究超大规模车间作业的高效调度问题。超大规模的生产作业中,由于调度规模较大,一些非主要联系的生产调度之间存在可能诱发主要调度联系冲突的可能。传统的基于遗传算法的车间作业调度方法在应用到超大规模车间作业调度环境下时,由于冲突的存在很难建立准确的调度模型,使得模型陷入收敛效率过低,早熟等缺陷,调度效率降低。为解决上述问题,提出一种最优家族遗传算法的超大规模车间生产调度方式。通过在种群优良个体附近构造最优家族,在相应的调度家族微空间中进行精确搜索,从而缩小了搜索范围。仿真结果表明,改进算法对大规模的车间调度具有搜索速度快、稳定性强的特点,提高了调度的效率。Large scale of operations research workshop efficient scheduling problem. Large scale production operation, the large scale of scheduling, some of the main contact between the production scheduling may cause major scheduling conflict may contact the. The traditional genetic algorithm based on the workshop scheduling problem in application method to large scale workshop scheduling problem environment, due to the existence of this conflict is difficult to establish accurate scheduling model, make model into convergence efficiency is low, the precocious, defects, scheduling low efficiency. Therefore put forward a kind of genetic algorithm the optimum family very large scale workshop production scheduling method. Through the population near the optimal structure excellent individual family, in the corresponding scheduling micro space accurately family search and reduce the search range. The simulation results show that the algorithm to large workshop scheduling with search speed, strong stability characteristics, improve the efficiency of the scheduling.

关 键 词：作业调度问题 最优家族遗传算法 收敛性 早熟现象 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP301[自动化与计算机技术—控制科学与工程]