考虑学习效应的多目标流水车间调度问题

发布时间：2018-04-03 12:13

本文选题：学习效应　切入点：流水车间调度　出处：《系统管理学报》2017年06期

【摘要】：考虑了学习效应对流水车间调度问题的影响,以最大完工时间和总加权滞后时间为优化目标,建立了与加工顺序相关的对数线性调度模型,设计了LNEH(NEH heuristic with Learning effect)启发式算法和MCF(Membrane Computing for multi-objective Flow-shop scheduling)算法进行求解。LNEH算法根据对学习效应下问题性质的分析,在初始序列和工件插入两个环节进而达到对原有NEH算法的改进,同时采用随机策略以获得多个解。MCF算法是采用膜计算理论设计的一种近似调度优化算法,针对调度问题设计了字符对象的编码方式,根据前端等级大小将字符对象往复地分配为均匀的对象集,借鉴PSO算法制订膜内规则:从外部档案和所在的基本膜内中分别选择作为参考的选择规则和类似于PSO算法的移动规则。数值仿真显示,不同学习系数对调度结果具有较大影响,并对比证明了所提两种算法的有效性。
[Abstract]:Considering the influence of learning effect on income job shop scheduling problem, a logarithmic linear scheduling model related to machining sequence is established with the maximum completion time and total weighted delay time as the optimization objectives.In this paper, LNEH(NEH heuristic with Learning effect-) heuristic algorithm and MCF(Membrane Computing for multi-objective Flow-shop scheduling algorithm are designed to solve .LNEH algorithm. According to the analysis of the nature of the problem under the learning effect, two links are inserted in the initial sequence and the workpiece to improve the original NEH algorithm.At the same time, using random strategy to obtain multiple solutions. MCF algorithm is an approximate scheduling optimization algorithm based on membrane computing theory. The encoding method of character object is designed for scheduling problem.The character objects are allocated back and forth as a uniform set of objects based on the size of the front end level,Drawing on the PSO algorithm to formulate the intramembrane rules: select the selection rules for reference from the external files and the basic film and the moving rules similar to the PSO algorithm respectively.Numerical simulation shows that different learning coefficients have great influence on the scheduling results, and the effectiveness of the two algorithms is proved by comparison.
【作者单位】：上海工程技术大学管理学院;上海交通大学安泰经济与管理学院;伯明翰大学商学院;
【基金】：国家自然科学基金资助项目(71371123,71502101,71632008,1271138) 上海市哲学社会科学规划课题(2017EGL009) 上海工程技术大学科研启动项目(校启2017-27) Europe-China High Value Engineering Network(EC-HVEN:295130)
【分类号】：TB497

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