[1]轩 华,熊梦莹,曹 颖.基于改进灰狼优化算法的分布式混合流水线重调度[J].郑州大学学报(工学版),2025,46(04):47-54.[doi:10.13705/j.issn.1671-6833.2025.04.020]
　XUAN Hua,XIONG Mengying,CAO Ying.Distributed Hybrid Flowline Rescheduling Based on Improved Gray Wolf Optimization Algorithm[J].Journal of Zhengzhou University (Engineering Science),2025,46(04):47-54.[doi:10.13705/j.issn.1671-6833.2025.04.020]
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基于改进灰狼优化算法的分布式混合流水线重调度()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年04期
页码:
47-54
栏目:
出版日期:
2025-07-10

文章信息/Info

Title:
Distributed Hybrid Flowline Rescheduling Based on Improved Gray Wolf Optimization Algorithm
文章编号:
1671-6833(2025)04-0047-08
作者:
轩 华1;  熊梦莹1;  曹 颖2
1. 郑州大学 管理学院,河南 郑州 450001;2. 河南科技大学 土木建筑学院,河南 洛阳 471000
Author(s):
XUAN Hua1;  XIONG Mengying1;  CAO Ying2
1. School of Management, Zhengzhou University, Zhengzhou 450001, China; 2. School of Civil Engineering and Architecture, Henan University of Science and Technology, Luoyang 471000, China
关键词:
分布式混合流水线重调度;  机器故障;  运输时间;  能耗;  改进灰狼优化算法
Keywords:
distributed hybrid flowline rescheduling;  machine breakdown;  transportation time;  energy consumption;  improved gray wolf optimization algorith
分类号:
TB49
DOI:
10.13705/j.issn.1671-6833.2025.04.020
文献标志码:
A
摘要:
针对机器故障和运输时间约束下的分布式混合流水线重调度问题,提出一种改进灰狼优化算法,以同时最小化最大完工时间、总能耗和总延期为优化目标构建整数规划模型。 首先,根据问题特点,设计了基于工厂-工序机器的三链式编码方式,提出了结合 NEH 启发式法和完全随机程序的种群初始化方法;其次,筛选领导层个体后,引入一种基于跟踪和自主行动的双模式并行搜索方法更新底层狼;最后,应用融合工序链前插变换和机器链后移操作的禁忌搜索以避免陷入局部最优。 仿真实验测试了 370 个算例,验证了所提算法改进项的有效性,与已有的混合白鲸优化算法、混合花朵授粉算法、经典灰狼算法和改进飞蛾-火焰优化算法相比,改进灰狼优化算法目标值分别改进了 9. 33%,12. 24%,10. 43%和 9. 61%,这说明了所提算法的有效性。
Abstract:
Distributed hybrid flowline rescheduling was investigated considering machine breakdown and transportation time constraints. An integer programming model was constructed with the optimization objective of simultaneously minimizing the maximum completion time, total energy consumption, and total delay. An improved grey wolf optimization algorithm was then proposed to solve it. Firstly, according to the characteristics of the problem, a three-chain encoding method based on factory-operation-machine was designed. A population initialization method combined with NEH heuristic approach and completely random procedure was proposed. Next, after the leadership individuals were chosen, a dual-mode parallel search method based on tracking and autonomous action was introduced to update the bottom wolves. Finally, tabu search integrated with forward insertion transformation of operation chain and backward shift operation of machine chain was applied to avoid falling into local optimum. Simulation experiments tested 370 instances. The effectiveness of the improvement items in the proposed algorithm was verified. The improved grey wolf optimization algorithm improved by 9. 33%, 12. 24%, 10. 43%, and 9. 61%, respectively, compared with the four algorithms, including hybrid beluga whale optimization algorithm, hybrid flower pollination algorithm, classical grey wolf optimization algorithm and improved moth-flame optimization algorithm. It illustrated the effectiveness of the proposed algorithm.

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更新日期/Last Update: 2025-07-13