Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (5): 17-20.DOI: 10.3778/j.issn.1002-8331.2010.05.006

• 博士论坛 • Previous Articles     Next Articles

Optimal manufacturing tasks assignment method based on multi-objectives

ZENG Qiang1,2,YANG Yu1,WANG Xiao-lei1,LIANG Xue-dong1   

  1. 1.State Key Laboratory of Mechanical Transmissions,Chongqing University,Chongqing 400030,China
    2.Henan Polytechnic University,Jiaozuo,Henan 454000,China
  • Received:2009-10-22 Revised:2009-12-08 Online:2010-02-11 Published:2010-02-11
  • Contact: ZENG Qiang

一类基于多个目标的制造任务优化分配方法

曾 强1,2,杨 育1,王小磊1,梁学栋1   

  1. 1.重庆大学 机械传动国家重点实验室,重庆 400030
    2.河南理工大学,河南 焦作 454000
  • 通讯作者: 曾 强

Abstract: An optimal manufacturing tasks assignment method based on multi-objectives is proposed.In the method,an optimal tasks assignment model is established.The optimal objective is total processing time minimization,end time minimization,total quality maximization and the restriction condition is to guarantee the load rate of every manufacturing equipment to be in the required scope.Considering the model’s characteristic of multi-objective,multi-restriction and large solution space,combining the advantage of genetic algorithm(good at global optimization) with that of simulated annealing algorithm(good at local optimization),a hybrid algorithm named Adaptive Genetic Algorithm based on Multi-Stage Parents’Replacement-Simulated Annealing Algorithm(MSPRAGA-SAA) is proposed.The model and algorithm are applied to a tasks assignment example in a multi-type and small-batch production manufacturing workshop.The application result validates the effectiveness of the method.

Key words: optimal tasks assignment, multi-objective decision, multi-stage parents’replacement, adaptive genetic algorithm, si-
mulated annealing algorithm

摘要: 提出了一种基于多个目标的制造任务优化分配方法。建立了以任务总加工时间最少、任务完成时间最早、任务完成总质量最高为目标函数,以制造设备负荷率满足要求为约束的优化分配模型。针对模型的多目标、多约束、大组合量特点,结合遗传算法全局搜索能力强、模拟退火算法局部搜索能力强的优点,提出了一种多阶段父代更新自适应遗传-模拟退火算法。以某多品种小批量生产车间制造任务分配为例,验证了方法的有效性。

关键词: 任务优化分配, 多目标决策, 多阶段父代更新, 自适应遗传, 模拟退火

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