|本期目录/Table of Contents|

[1]何军红,马国伟,刘 赛,等.MES柔性作业车间调度优化算法的研究[J].工业仪表与自动化装置,2020,(01):26-32.
　HE Junhong,MA Guowei,LIU Sai,et al.Research on MES flexible job shop scheduling optimization algorithm[J].Industrial Instrumentation & Automation,2020,(01):26-32.
点击复制

MES柔性作业车间调度优化算法的研究

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2020年01期
页码:
26-32
栏目:
出版日期:
2020-02-15

文章信息/Info

Title:
Research on MES flexible job shop scheduling optimization algorithm
作者:
何军红; 马国伟; 刘 赛; 张 迪
西北工业大学 航海学院，西安 710072
Author(s):
HE Junhong;  MA Guowei;  LIU Sai;  ZHANG Di
School of Marine Science and Technology,Northwestern Polytechnical University, Xi’an 710072, China
关键词:
多阶段优化调度算法; 分层式编码; 适应度
Keywords:
multi-stage optimization scheduling algorithm;  hierarchical coding;  adaptability
分类号:
TP274+.2
DOI:
-
文献标志码:
A
摘要:
MES即制造企业生产过程执行系统，是一套面向制造企业车间执行层的生产信息化管理系统。针对MES生产调度模块的柔性作业车间调度问题，提出一种改进的多阶段优化调度算法。在算法的第一阶段借鉴了基于工序顺序与基于机器相结合的编码方式，提出了基于矩阵序列的分层式编码方式，在选择操作前设计一种基于第三层高位编码值的淘汰机制；第二阶段结合通过删减网络的神经元或连接来降低网络复杂度的思想，提出一种遗传算法与神经网络算法结合的小范围竞争择优策略，并在交叉操作中提出了双层交叉操作以及分层交叉操作；第三阶段在变异操作后增加一种基于相似度值提高种群多样性的方法。经过仿真实验证明了该算法的优良性。
Abstract:
MES is a production information management system for the execution layer of the manufacturing enterprise.Aiming at the flexible job-shop scheduling problem of MES production scheduling module,an improved multi-stage optimization scheduling algorithm was proposed.In the first stage of the algorithm,the coding method based on the combination of process sequence and machine-based was used.A hierarchical coding method based on matrix sequence was proposed.A phase-out mechanism based on the third-level high-order code value was designed before the selection operation.In the second stage,with the idea of reducing the complexity of the network by deleting the neurons or connections of the network,a small-scale competitive optimization strategy combining genetic algorithm and neural network algorithm was proposed, and a double-layer crossover operation was proposed in the crossover operation and layered crossover operation. In the third stage, a method of increasing the diversity of population based on similarity value was added after variation operation. The simulation results have proved the superiority of the algorithm.

参考文献/References:

[1] 李澍,吴瑜,杨立斌.面向离散企业制造执行系统关键技术研究[J].控制工程,2015,22(S1):13-16.

[2] Liqi Zhang,Lingfa Lu,Jinjiang Yuan.Two-machine open- shop scheduling with rejection to minimize the makespan[J]. OR Spectrum,2016,38(2):69-81.
[3] Gao K Z, Suganthan P N, Pan Q K, et al. Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion[J]. KNOWLEDGE-BASED SYSTEMS,2016,109: 1-16.
[4] Knopp S, Dauzere-Peres S, Yugma C. A batch-oblivious approach for Complex Job-Shop scheduling problems[J]. Design Automation for Embedded Systems,2017,263(1): 50-61.
[5] Shen J, Wang L, Deng J, et al. A Pareto-Based Discrete Harmony Search Algorithm for Bi-objective Reentrant Hybrid Flowshop Scheduling Problem[M].Advances in Intelligent Systems and Computing,2016: 382,435-445.
[6] 李帆,高东,许欣,等.改进蝙蝠算法柔性作业车间调度问题研究[J].计算机工程与应用,2018,54(21):265-270.
[7] 肖华军,柴子力,张超勇,等.基于混合化学反应算法的柔性作业车间调度[J].计算机集成制造系统,2018,24(09): 2234-2245.
[8] Zhang H, Xie J, Ge J, et al. A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar[J]. EUROPEAN JOURNAL of OPERATIONAL RESEARCH,2019,272(3): 868-878.
[9] Agarwal M. Genetic Algorithm-Enabled Particle Swarm Optimization (PSOGA)-Based Task Scheduling in Cloud Computing Environment[J].INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, 2018,17(4): 1237-1267.
[10] Zheng X, Wang L. A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem[J]. KNOWLEDGE-BASED SYSTEMS, 2014,57: 95-103.
[11] Celli G, Pilo F, Pisano G, et al. Distribution energy storage investment prioritization with a real coded multi-objective Genetic Algorithm[J]. ELECTRIC POWER SYSTEMS RESEARCH. 2018, 163(A): 154-163.
[12] Wang K, Choi S H, Lu H. A hybrid estimation of distribution algorithm for simulation-based scheduling in stochastic permutation flowshop[J].COMPUTERS & INDUSTRIAL ENGINEERING, 2015,90: 186-196.
[13] Meng T, Pan Q, Sang H. A hybrid artificial bee colony algorithm for a flexible job shop scheduling problem with overlapping in operations[J].INTERNATIONAL JOUR- NAL of PRODUCTION RESEARCH,2018,56(16): 5278-5292.
[14] Xanthopoulos A S, Koulouriotis D E. Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing[J]. JOURNAL of INTELLIGENT MANUFACTURING, 2018, 29(1): 69-91.
[15] Jiménez-Carrión M. Algoritmo Genético Simple para Resolver el Problema de Programación de la Tienda de Trabajo(Job Shop Scheduling)[J]. Información tecnológica, 2018, 29(5): 299-314.
[16] Kacem I, Hammadi S, Borne P. Pareto-optimality approach for flexible job-shop scheduling problems: hybridiza-tion of evolutionary algorithms and fuzzy logic[J]. MATHEMATICS and COMPUTERS IN SIMULATION, 2002,60(5): 245-276.
[17] 栾飞,王雯,傅卫平,等.基于PST层次结构的改进GA求解柔性车间调度问题[J].计算机集成制造系统,2014, 20(10): 2494-2501.
[18] 孔飞,吴定会,纪志成.基于双层粒子群优化算法的柔性作业车间调度优化[J].计算机应用,2015,35(02):476-480.
[19] 薛宏全,魏生民,张鹏,等.基于多种群蚁群算法的柔性作业车间调度研究[J].计算机工程与应用,2013,49(24): 243-248+261.

相似文献/References:

备注/Memo

备注/Memo:
收稿日期：2019-05-09
基金项目：工信部绿色制造系统集成项目
作者简介：何军红(1971),男,博士,研究生导师,西北工业大学智能工业和信息化研究所所长,研究领域为控制工程、工业网络、智能制造、工业大数据、能源管理等技术。
更新日期/Last Update: 2020-01-10