集成工人和AGV的多要素柔性作业车间调度方法

doi:10.3901/JME.2025.18.330

机械工程学报 ›› 2025, Vol. 61 ›› Issue (18): 330-343.doi: 10.3901/JME.2025.18.330

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集成工人和AGV的多要素柔性作业车间调度方法

方遒^1,2, 宋豪杰^1,2, 卢弘^1,2, 毛建旭^1,2, 王耀南^1,2

1. 湖南大学机器人视觉感知与控制技术国家工程研究中心长沙 410082;
2. 湖南大学电气与信息工程学院长沙 410082

收稿日期:2024-05-28 修回日期:2024-11-09 发布日期:2025-11-08
作者简介:方遒，男，1990年出生，博士，副教授，博士研究生导师。主要研究方向为复杂工业过程建模与优化。E-mail：qfang@hnu.edu.cn;宋豪杰，男，2003年出生，硕士研究生。主要研究方向为智能车间调度。E-mail：songhaojie@hnu.edu.cn;卢弘(通信作者)，男，1992年出生，博士，助理研究员。主要研究方向为生产调度和智能优化方法。E-mail：luhong@hnu.edu.cn;毛建旭，男，1974年出生，博士，教授，博士研究生导师。主要研究方向为计算机视觉、图像处理和模式识别。E-mail：maojianxu@hnu.edu.cn;王耀南，男，1957年出生，博士，教授，博士研究生导师，中国工程院院士。主要研究方向为机器人学、智能控制和图像处理。E-mail：yaonan@hnu.edu.cn
基金资助:
湖南创新型省份建设科技重大专项(2021GK1010)、国家自然科学基金(62293510)、湖南省自然科学基金(2023JJ30162)、岳麓山工业创新中心重大(2023YCII0102)、湖南省教育厅科学研究项目优秀青年(23B0029)和湖南省中央引导地方科技发展资金(2023ZYT003-1)资助项目

Research on Multi-factors Flexible Job Shop Scheduling Problem with Workers and AGVs

FANG Qiu^1,2, SONG Haojie^1,2, LU Hong^1,2, MAO Jianxu^1,2, WANG Yaonan^1,2

1. National Engineering Research Center of RVC, Hunan University, Changsha 410082;
2. College of Electrical and Information Engineering, Hunan University, Changsha 410082

Received:2024-05-28 Revised:2024-11-09 Published:2025-11-08

摘要/Abstract

摘要： 面向集成多种生产要素的智能车间，实现各类资源高效调度以完成任务，对提高生产效率具有重要意义和价值。针对一类考虑多要素的柔性作业车间调度问题，提出一种高效的混合进化算法。首先，基于对问题背景和多要素运行情况的分析，以最小化最大完工时间为目标，构建了含工件、机器、AGV和工人四种生产要素的柔性作业车间调度模型。然后，针对模型中不同决策变量的特点，提出结合启发式和随机式的混合初始化策略，以生成高质量的初始种群。根据个体的四层编码结构，设计基于经典遗传算子的全局搜索方法。针对易于陷入局部最优解的困境，提出记忆机制导向的多邻域局部搜索方法，以增强算法的局部搜索能力。最后，基于标准测试集生成了多组适用的算例，并设计一系列试验以验证算法的性能。试验结果表明，混合初始化策略和多邻域局部搜索能够有效地改善算法性能。与领域中多种先进的算法比较，所提算法在调度方案质量上更具优越性。

关键词: 多生产要素, 柔性作业车间调度, 混合进化算法, 启发式初始化, 多邻域局部搜索

Abstract: It is of great significance to efficiently scheduling various resources to complete tasks for intelligent workshops with multiple production factors. An efficient hybrid evolutionary algorithm is proposed to solve a flexible job shop scheduling problem with multiple production factors. Firstly, a MFFJSP-WA model incorporating four production factors—jobs, machines, AGVs, and workers—is constructed with the objective of minimizing the maximum completion time based on the analysis of the problem background and the operation conditions of multiple factors. Since the model includes four kinds of decision variables, the hybrid initialization strategy combining heuristic and random methods is proposed to generate a high-quality initial population. A global search method based on classical genetic operators is designed according to the four-layer encoding structure of individuals. To address the issue of easily falling into local optimum, a multi-neighborhood local search method guided by a memory mechanism is proposed to enhance the algorithm's local search capability. Finally, the proposed algorithm is tested on sets of instances expanded from benchmarks. The experimental results show that the hybrid initialization strategy and local search operation can effectively improve the algorithm’s performance. Compared with various advanced algorithms in the field, the proposed algorithm is superior in solution quality performance.

Key words: multiple production factors, flexible job shop scheduling, hybrid evolutionary algorithm, heuristic initialization, multi-neighborhood local search

中图分类号:

TP301

方遒, 宋豪杰, 卢弘, 毛建旭, 王耀南. 集成工人和AGV的多要素柔性作业车间调度方法[J]. 机械工程学报, 2025, 61(18): 330-343.

FANG Qiu, SONG Haojie, LU Hong, MAO Jianxu, WANG Yaonan. Research on Multi-factors Flexible Job Shop Scheduling Problem with Workers and AGVs[J]. Journal of Mechanical Engineering, 2025, 61(18): 330-343.

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集成工人和AGV的多要素柔性作业车间调度方法

Research on Multi-factors Flexible Job Shop Scheduling Problem with Workers and AGVs

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