[1]杨智飞,苏春,胡祥涛,等.面向智能生产车间的多AGV系统多目标调度优化[J].东南大学学报(自然科学版),2019,49(6):1033-1040.[doi:10.3969/j.issn.1001-0505.2019.06.003]
 Yang Zhifei,Su Chun,Hu Xiangtao,et al.Multi-objective scheduling optimization for multi-AGV systems of intelligent jobshop[J].Journal of Southeast University (Natural Science Edition),2019,49(6):1033-1040.[doi:10.3969/j.issn.1001-0505.2019.06.003]
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面向智能生产车间的多AGV系统多目标调度优化()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
49
期数:
2019年第6期
页码:
1033-1040
栏目:
自动化
出版日期:
2019-11-20

文章信息/Info

Title:
Multi-objective scheduling optimization for multi-AGV systems of intelligent jobshop
作者:
杨智飞1苏春1胡祥涛2陈帝江2
1东南大学机械工程学院, 南京 211189; 2中国电子科技集团第三十八研究所, 合肥 230088
Author(s):
Yang Zhifei1 Su Chun1 Hu Xiangtao2 Chen Dijiang2
1School of Mechanical Engineering, Southeast University, Nanjing 211189, China
2No.38 Research Institute of China Electronics Technology Group Corporation, Hefei 230088, China
关键词:
自动导引车 多目标优化 多目标遗传算法 智能调度
Keywords:
automatic guided vehicle(AGV) multi-objective optimization multi-objective genetic algorithm(MOGA) intelligent scheduling
分类号:
TP278
DOI:
10.3969/j.issn.1001-0505.2019.06.003
摘要:
以具有多台自动导引车(AGV)的智能生产车间为对象,以完工时间、AGV数量以及惩罚成本的最小化作为优化目标,构建作业车间多目标调度优化模型.针对多目标调度优化模型的求解需求,提出一种自适应多目标遗传-差分进化算法(AMOGA-DE),采用多段式实数编码的染色体表征调度方案,利用遗传算法获得模型优化解,融合差分进化算法和外部Pareto解集档案构建技术以改进解的质量,引入自适应策略以提高算法的收敛速度,实现多约束条件下AGV系统的多目标调度优化.以一个具有多台AGV的智能制造车间为例进行案例分析,得到调度方案.将AMOGA-DE与NSGA-Ⅱ、SPEA2算法应用于3个不同规模问题,研究结果表明:AMOGA-DE算法具有更快的收敛速度,能得到更好的优化结果,在不同规模的算例上获得了分布均匀且具有较高质量的Pareto解集.
Abstract:
Aiming at minimizing the makespan, the number of automatic guided vehicles(AGVs)and penalty cost, a multi-objective optimization mathematical model was established for the intelligent jobshop with AGVs. An adaptive multi-objective genetic algorithm with differential evolution(AMOGA-DE)was proposed to solve the optimal model. A multi-stage real number coding method was used to encode the chromosome, and the genetic algorithm(GA)was applied to generate the optimal solutions. The differential evolution(DE)algorithm and the external Pareto solution archive constructing technique were integrated to improve the quality of the optimal solutions, and the self-adaptive strategy was adopted to enhance the convergence speed of multi-objective scheduling optimization under multiple-constraints. Selecting an intelligent manufacturing jobshop with AGVs as an example, a case study was carried out, and the corresponding scheduling plans were obtained. Compared with the results of the fast elite non-dominated sorting genetic algorithm(NSGA-Ⅱ)and the strength Pareto evolutionary algorithm 2(SPEA2)in three different scale cases, the feasibility and effectiveness of the proposed algorithm were verified, and the Pareto solution sets with a uniform distribution and higher quality were obtained in different scale cases.

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备注/Memo

备注/Memo:
收稿日期: 2019-04-08.
作者简介: 杨智飞(1995—),男,硕士生;苏春(联系人),男,博士,教授,博士生导师,suchun@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(71671035)、国防基础科研计划资助项目(JCKY2016210C007).
引用本文: 杨智飞,苏春,胡祥涛,等.面向智能生产车间的多AGV系统多目标调度优化[J].东南大学学报(自然科学版),2019,49(6):1033-1040. DOI:10.3969/j.issn.1001-0505.2019.06.003.
更新日期/Last Update: 2019-11-20