工业工程

工业工程 ›› 2024, Vol. 27 ›› Issue (3): 106-113.doi: 10.3969/j.issn.1007-7375.230125

• 智能制造系统与车间调度优化 • 上一篇    下一篇

求解燃气轮机制造车间调度的混合和声搜索算法

李明辉1, 石宇强1, 石小秋1,2, 李佳1   

  1. 1. 西南科技大学 制造科学与工程学院，四川 绵阳 621010;
    2. 华中科技大学 智能制造装备与技术全国重点实验室，湖北 武汉 430074
  • 收稿日期:2023-06-08 发布日期:2024-07-12
  • 作者简介:李明辉 (1989—)，男，黑龙江省人，博士研究生，主要研究方向为机器学习及其应用、智能调度算法
  • 基金资助:
    四川省自然科学基金资助项目 (2023NSFSC0507)； “智能制造装备与技术全国重点实验室” (华中科技大学) 开放课题资助项目 (IMETKF2023026)

A Hybrid Harmony Search Algorithm for Scheduling in a Gas Turbine Manufacturing Workshop

LI Minghui1, SHI Yuqiang1, SHI Xiaoqiu1,2, LI Jia1   

  1. 1. School of Manufacturing Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, China;
    2. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
  • Received:2023-06-08 Published:2024-07-12

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摘要： 燃气轮机生产属于典型的离散型制造，其多品种小批量的生产特点给车间作业调度带来挑战，导致企业生产效率低下，不能满足产品交货期。因和声搜索算法结构简单易操作，常用于解决此类作业车间调度问题。然而传统和声搜索算法收敛速度较慢，易陷入局部最优。本文构建以最小化最大完工时间为目标的燃气轮机制造车间调度数学模型，提出一种离散型改进多种群混合和声搜索算法进行求解。结合和声搜索算法与变邻域搜索算法的优点，采用基于工序的编码方式进行编码，在种群更新部分引入模拟退火的Metropolis接受准则，提高种群多样性；提出自适应的记忆库保留概率和音调调节率来调节参数，以提高算法的全局寻优能力；加入变邻域搜索以提高算法的收敛速度。通过性能测试及实例验证表明，相较于已有算法，所提算法具有更好的性能。

关键词: 燃气轮机制造车间调度, 和声搜索算法 (HS), 变邻域搜索 (VNS), Metropolis准则

Abstract: Gas turbine production is a typical kind of discrete manufacturing. The production characteristics of multiple varieties and small batches present challenges to workshop scheduling, resulting in low production efficiency and difficulties of meeting product delivery deadlines. The Harmony Search (HS) algorithm is often used to solve such workshop scheduling problems due to its simplicity and ease of operation. However, the convergence rate of traditional HS algorithm is relatively low, and it is easy to get trapped in local optima. Accordingly, this paper builds a mathematical model for scheduling in a gas turbine manufacturing workshop with the objective of minimizing the maximum completion time. A discrete improved multi-population hybrid HS algorithm is proposed to solve the problem. Combining the advantages of HS algorithm and the variable neighborhood search algorithm, we propose an encoding method based on operations. The Metropolis rule of simulated annealing is used in population iteration to improve population diversity. An adaptive memory retention probability and pitch adjusting rate are proposed to adjust parameters, improving the global optimization capability of the algorithm. We also incorporate variable neighborhood searching to accelerate the convergence of the proposed algorithm. Performance tests and case studies show that the proposed algorithm outperforms existing algorithms.

Key words: gas turbine manufacturing workshop scheduling, harmony search (HS) algorithm, variable neighborhood search (VNS), Metropolis rules

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