求解分布式柔性作业车间调度的混合蛙跳算法

doi:10.3901/JME.2021.17.263

机械工程学报 ›› 2021, Vol. 57 ›› Issue (17): 263-272.doi: 10.3901/JME.2021.17.263

• 数字化设计与制造 • 上一篇

扫码分享

求解分布式柔性作业车间调度的混合蛙跳算法

孟磊磊¹, 张彪¹, 任亚平², 张超勇³

1. 聊城大学计算机学院聊城 252059;
2. 暨南大学智能科学与工程学院珠海 519070;
3. 华中科技大学数字制造装备与技术国家重点实验室武汉 430074

收稿日期:2020-09-05 修回日期:2020-11-05 发布日期:2021-11-16
通讯作者: 张超勇(通信作者),男,1972年出生,博士,教授,博士研究生导师。主要研究方向为车间调度、网络化制造和绿色制造。E-mail:zcyhust@mail.hust.edu.cn
作者简介:孟磊磊,男,1991年出生,博士,讲师。主要研究方向为车间调度、刀具磨损、绿色制造。E-mail:mengleilei@lcu-cs.com
基金资助:
国家自然科学基金资助项目（51861165202，51575211，51705263）。

Hybrid Shuffled Frog-leaping Algorithm for Distributed Flexible Job Shop Scheduling

MENG Leilei¹, ZHANG Biao¹, REN Yaping², ZHANG Chaoyong³

1. School of Computer Science, Liaocheng University, Liaocheng 252059;
2. School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070;
3. State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074

Received:2020-09-05 Revised:2020-11-05 Published:2021-11-16

摘要/Abstract

摘要： 针对分布式柔性作业车间（Distributed flexible job shop scheduling problem，DFJSP）最小化最大完工时间问题，提出一种混合蛙跳算法。在该算法中，编码采用基于工序序列和工厂序列的部分解空间编码方案，机床选择在解码过程中通过规则确定。引入变邻域搜索算法提升蛙跳算法的局部搜索能力。为了弥补部分解空间编码不能探索整个解空间的缺点，引入针对关键工厂的全解空间禁忌搜索，从而扩大算法解空间、进一步提升算法的局部搜索能力。通过对基准实例的求解，并与现有的最先进算法进行对比，验证了所提算法的有效性和优越性。

关键词: 分布式调度, 柔性作业车间, 蛙跳算法, 最大完工时间

Abstract: Aiming at the distributed flexible job shop scheduling problem (DFJSP) with minimizing makespan, a hybrid shuffled frog-leaping algorithm (HSFLA) is proposed. In this algorithm, the encoding adopts the scheme of partial solution space based on the operation and factory sequences, and the machine selection is determined in the decoding process with a rule. A variable neighborhood search (VNS) algorithm is introduced to improve the local search ability of HSFLA. To make up for the shortcoming that the partial space encoding method cannot search the full solution space, a tabu search algorithm of full solution space is introduced in critical factory to expand the solution space and further improve the local search ability of HSFLA. By solving benchmark instances and comparing HSFLA with the existing state-of-the-art algorithms, the effectiveness and superiority of HSFLA are proved.

Key words: distributed scheduling, flexible job shop, shuffled frog-leaping algorithm, makespan

中图分类号:

TP18

孟磊磊, 张彪, 任亚平, 张超勇. 求解分布式柔性作业车间调度的混合蛙跳算法[J]. 机械工程学报, 2021, 57(17): 263-272.

MENG Leilei, ZHANG Biao, REN Yaping, ZHANG Chaoyong. Hybrid Shuffled Frog-leaping Algorithm for Distributed Flexible Job Shop Scheduling[J]. Journal of Mechanical Engineering, 2021, 57(17): 263-272.

参考文献

[1] LUO Q,DENG Q,GONG G,et al. An efficient memetic algorithm for distributed flexible job shop scheduling problem with transfers[J]. Expert Systems with Applications, 2020,160:113721.
[2] HUANG J,PAN Q,GAO L. An effective iterated greedy method for the distributed permutation flowshop scheduling problem with sequence-dependent setup times[J]. Swarm and Evolutionary Computation,2020,59:100742.
[3] WANG G,GAO L,LI X,et al. Energy-efficient distributed permutation flow shop scheduling problem using a multi-objective whale swarm algorithm[J]. Swarm and Evolutionary Computation,2020,57:100716.
[4] PAN Q,GAO L,WANG L,et al. Effective heuristics and metaheuristics to minimize total flowtime for the distributed permutation flowshop problem[J]. Expert Systems with Applications,2019,124:309-324.
[5] MENG L,ZHANG C,REN Y,et al. Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem[J]. Computers & Industrial Engineering,2020,142:106347.
[6] NADERI B,AZAB A. An improved model and novel simulated annealing for distributed job shop problems[J]. The International Journal of Advanced Manufacturing Technology,2015,81(1-4):693-703.
[7] NADERI B,AZAB A. Modeling and heuristics for scheduling of distributed job shops[J]. Expert Systems with Applications,2014,41(17):7754-7763.
[8] 吴秀丽,刘夏晶. 差分进化算法求解分布式柔性作业车间调度问题[J]. 计算机集成制造系统,2019,25(10):2539-2558.WU Xiuli,LIU Xiajing. Differential evolution algorithm for solving distributed flexible job shop scheduling problem[J]. Computer Integrated Manufacturing Systems,2019,25(10):2539-2558.
[9] 吴锐,郭顺生,李益兵,等. 改进人工蜂群算法求解分布式柔性作业车间调度问题[J]. 控制与决策,2019,34(12):2527-2536.WU Rui,GUO Shunsheng,LI Yibing,et al. An improved artificial bee colony algorithm for distributed and flexible job-shop scheduling problem[J]. Control and Decision, 2019,34(12):2527-2536.
[10] CHAN F T S,CHUNG S H,CHAN L Y,et al. Solving distributed FMS scheduling problems subject to maintenance:Genetic algorithms approach[J]. Robotics and Computer-Integrated Manufacturing,2006,22(5-6):493-504.
[11] CHAN F T S,CHUNG S H,CHAN P L Y. Application of genetic algorithms with dominant genes in a distributed scheduling problem in flexible manufacturing systems[J]. International Journal of Production Research,2006,44(3):523-543.
[12] De GIOVANNI L,PEZZELLA F. An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem[J]. European Journal of Operational Research,2010,200(2):395-408.
[13] 杨江波,陈友玲,曹楠. 面向柔性作业分布式车间的分层调度模型研究[J]. 计算机工程与应用,2014,50(23):239-244.YANG Jiangbo,CHEN Youling,CAO Nan. Research on grading scheduling model of flexible job distributed workshop[J]. Computer Engineering and Applications,2014,50(23):239-244.
[14] ZIAEE M. A heuristic algorithm for the distributed and flexible job-shop scheduling problem[J]. The Journal of Supercomputing,2014,67(1):69-83.
[15] LU P,WU M,TAN H,et al. A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems[J]. Journal of Intelligent Manufacturing,2018,29(1):19-34.
[16] CHANG H,LIU T. Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms[J]. Journal of Intelligent Manufacturing,2017,28(8):1973-1986.
[17] WU M,LIN C,LIN C,et al. Effects of different chromosome representations in developing genetic algorithms to solve DFJS scheduling problems[J]. Computers & Operations Research,2017,80:101-112.
[18] LIN C,LEE I,WU M. Merits of using chromosome representations and shadow chromosomes in genetic algorithms for solving scheduling problems[J]. Robotics and Computer-Integrated Manufacturing,2019,58:196-207.
[19] 黄英杰,姚锡凡. 基于目标级联法和粒子群算法的柔性分布式车间调度[J]. 中南大学学报,2012,43(1):151-158.HUANG Yingjie,YAO Xifan. Planning and scheduling of multiple flexible-shops based on analytical target cascading and particle swarm optimization. Journal of Central South University,2012,43(1):151-158.
[20] EUSUFF M M,LANSEY K E. Optimization of water distribution network design using the shuffled frog leaping algorithm[J]. Journal of Water Resources Planning & Management,2003,129(3):210-225.
[21] PAN Q,WANG L,GAO L,et al. An effective shuffled frog-leaping algorithm for lot-streaming flow shop scheduling problem[J]. The International Journal of Advanced Manufacturing Technology,2011,52(5-8):699-713.
[22] LEI D,GUO X. A shuffled frog-leaping algorithm for job shop scheduling with outsourcing options[J]. International Journal of Production Research,2016,54(16):4793-4804.
[23] LEI D,ZHENG Y,GUO X. A shuffled frog-leaping algorithm for flexible job shop scheduling with the consideration of energy consumption[J]. International Journal of Production Research,2016,55(11):3126-3140.
[24] MASTROLILLI M,GAMBARDELLA L M. Effective neighbourhood functions for the flexible job shop problem[J]. Journal of Scheduling,2000,3(1):3-20.
[25] LI X,GAO L. An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem[J]. International Journal of Production Economics,2016,174:93-110.

求解分布式柔性作业车间调度的混合蛙跳算法

Hybrid Shuffled Frog-leaping Algorithm for Distributed Flexible Job Shop Scheduling

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	赵诗奎. Job Shop基于无延迟调度路径重连与回溯禁忌搜索算法研究[J]. 机械工程学报, 2021, 57(14): 291-303.
[2]	李聪波, 王睿, 寇阳, 吕岩, 赵希坤. 考虑设备预维护的柔性作业车间调度节能优化方法[J]. 机械工程学报, 2021, 57(10): 220-230.
[3]	赵诗奎. 作业车间调度问题的多工序联动邻域结构研究[J]. 机械工程学报, 2020, 56(13): 192-206.
[4]	石小秋, 李炎炎, 邓丁山, 龙伟. 基于自适应变级遗传杂草算法的FJSP研究[J]. 机械工程学报, 2019, 55(6): 223-232.
[5]	李明, 雷德明. 考虑准备时间和关键目标的柔性作业车间低碳调度研究[J]. 机械工程学报, 2019, 55(21): 139-149.
[6]	魏鑫, 张泽群, 唐敦兵, 杨长祺, 金永乔, 秦威. 面向节能的导弹结构件混线生产作业车间多目标调度研究[J]. 机械工程学报, 2018, 54(9): 45-54.
[7]	李聪波, 沈欢, 李玲玲, 易茜. 面向能耗的多工艺路线柔性作业车间分批优化调度模型^*[J]. 机械工程学报, 2017, 53(5): 12-23.
[8]	赵诗奎. 基于新型邻域结构的混合算法求解作业车间调度[J]. 机械工程学报, 2016, 52(9): 141-151.
[9]	徐华, 张庭. 混合离散蝙蝠算法求解多目标柔性作业车间调度^*[J]. 机械工程学报, 2016, 52(18): 201-212.
[10]	赵诗奎. 求解柔性作业车间调度问题的两级邻域搜索混合算法[J]. 机械工程学报, 2015, 51(14): 175-184.
[11]	彭建刚;刘明周;张铭鑫;张玺;葛茂根. 基于改进非支配排序的云模型进化多目标柔性作业车间调度[J]. , 2014, 50(12): 198-205.
[12]	肖海宁;楼佩煌;钱晓明;武星;满增光. 一种柔性作业车间环境下单向导引路径网络设计方法[J]. , 2013, 49(3): 122-129.
[13]	林晓华;冯毅雄;谭建荣;冯勇;高一聪. 基于量词约束满足的机械产品质量特性稳健优化设计方法[J]. , 2013, 49(15): 169-179.
[14]	施进发;焦合军;陈涛. 交货期惩罚下柔性车间调度多目标Pareto优化研究[J]. , 2012, 48(12): 184-192.
[15]	张超勇;董星;王晓娟;李新宇;刘琼. 基于改进非支配排序遗传算法的多目标柔性作业车间调度[J]. , 2010, 46(11): 156-164.