Research on Complex Hybrid Flow-Shop Scheduling Problem Solved by Improved Grey Wolf Optimizer

MENG Rong-hua; SUN Ai-wen; WU Zheng-jia; ZHU Ying-ying; CHEN Bao-jia; ZHANG Biao; RAO Yun-qing

doi:10.12263/DZXB.20191319

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Research on Complex Hybrid Flow-Shop Scheduling Problem Solved by Improved Grey Wolf Optimizer

PAPERS | 更新时间：2025-12-08

- Research on Complex Hybrid Flow-Shop Scheduling Problem Solved by Improved Grey Wolf Optimizer
- ACTA ELECTRONICA SINICA Vol. 49, Issue 8, Pages: 1515-1523(2021)
- 作者机构：
  
  1.三峡大学机械与动力学院，湖北宜昌 443002
  2.聊城大学计算机学院，山东聊城 252000
  3.华中科技大学机械科学与工程学院，湖北武汉 430074
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20191319
  CLC： TP391;
- Received：27 November 2019，
  
  Revised：2020-12-26，
  
  Published：25 August 2021
- 稿件说明：
移动端阅览
孟荣华,孙艾文,吴正佳等.改进灰狼算法求解复杂混合流水调度问题研究[J].电子学报,2021,49(08):1515-1523.

MENG Rong-hua,SUN Ai-wen,WU Zheng-jia,et al.Research on Complex Hybrid Flow-Shop Scheduling Problem Solved by Improved Grey Wolf Optimizer[J].ACTA ELECTRONICA SINICA,2021,49(08):1515-1523.
孟荣华,孙艾文,吴正佳等.改进灰狼算法求解复杂混合流水调度问题研究[J].电子学报,2021,49(08):1515-1523. DOI： 10.12263/DZXB.20191319.

MENG Rong-hua,SUN Ai-wen,WU Zheng-jia,et al.Research on Complex Hybrid Flow-Shop Scheduling Problem Solved by Improved Grey Wolf Optimizer[J].ACTA ELECTRONICA SINICA,2021,49(08):1515-1523. DOI： 10.12263/DZXB.20191319.

摘要

本文研究了带切割工序生产企业的工件调度优化问题.以最小化所有工件的最大完工时间为目标，建立了考虑一对多加工约束的混合流水调度问题的两阶段数学模型.设计了基于问题特征的协同奔袭灰狼算法，制定了新的编码规则和狼群分级策略，改进了探狼游走策略，并提出了猛狼协同奔袭策略.通过改进的标准算例对GA、GWO和CDGWO算法进行参数敏感性测试及求解对比.求解小规模问题时算法差异不明显，但是随着问题规模的增大，CDGWO求解效果性能稳定且进化效率较好.

Abstract

This paper studies an optimization problem of the production scheduling with cutting process. A two-stage mathematical model of hybrid flow-shop scheduling problem with one-to-many constraints is established to minimize the makespan of all jobs. A cooperative attacking GWO is designed according to the problem feature

a new encoding scheme and the social hierarchy are developed. The search for the prey strategy is improved

and the collaborative running attack strategy is proposed. The sensitivity test and the results comparison of GA

GWO and CDGWO are carried out by the improved standard test. Algorithms are not significantly different when solving small-scale problems. As the scale increasing

the CDGWO performs very stable and the evolutionary efficiency shows very well.

关键词

Keywords

references

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