基于改进贪婪随机自适应算法的车间调度优化  

Assembly Workshops Scheduling Optimization Based on Advanced Greedy Randomized Adaptive Search Procedure

作　　者：冯丽娟[1] 严洪森[1] 朱莉莉[1] 

机构地区：[1]东南大学自动化学院复杂工程系统测量与控制教育部重点实验室,江苏南京210096

出　　处：《计算机技术与发展》2009年第10期44-46,50,共4页Computer Technology and Development

基　　金：国家863计划资助项目(2007AA04Z112);国家自然科学基金资助项目(50875046)

摘　　要：贪婪随机自适应搜索算法(GRASP)是近年来涌现的新的元启发式算法,其在车间调度优化方面的应用还很少,且解的全局满意度不够好。在已有GRASP的基础上,提出一种改进GRASP来解决装配车间调度优化问题。将发动机装配线简化为一个flow shop问题,以装配作业完成的总加工时间最短为优化目标。在已有GRASP强化策略中融入优化集ε的自进化过程而获得改进GRASP,并用实例对改进GRASP进行了仿真研究。结果表明,与现有的GRASP和遗传算法相比,强化策略和优化集ε自进化过程的结合可以大大提高改进GRASP的全局满意度,对求解该类问题有很好的效果。The greedy randomized adaptive search procedure（GRASP） is a new metaheuristic for combinatorial optimization with few applications in workshops scheduling optimization and global satisfactory results is not good enough. On the basis of current GRASP,presents an advanced GRASP to solve assembly workshops scheduling optimization problems. An engine asssembly line is simplified into a flow shop, with the objective of minimizing the total assembly co, repletion time. An advanced GRASP is gotten by the combination of intensification strategy in present GRASP with self- evolution process of optimized set ε The simulation result of example shows that the advanced GRASP can greatly enhance the global satisfaction and is very suitable for solving, this problem, compared with the present GRASP and genetic algorithm.

关 键 词：FLOW SHOP 改进GRASP 强化策略 自进化过程 全局满意度 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]