电子学报 ›› 2021, Vol. 49 ›› Issue (2): 324-330.DOI: 10.12263/DZXB.20200382

• 学术论文 • 上一篇    下一篇

带运输资源约束柔性作业车间调度问题的人工蜂群算法

李俊青1,2, 杜宇1, 田杰1, 段培永1, 潘全科3,2   

  1. 1. 山东师范大学信息科学与工程学院, 山东济南 250014;
    2. 聊城大学计算机学院, 山东聊城 252059;
    3. 上海大学机电工程与自动化学院, 上海 200072
  • 收稿日期:2020-04-21 修回日期:2020-07-06 出版日期:2021-02-25 发布日期:2021-02-25
  • 作者简介:李俊青 男,1976年出生,山东聊城人.教授、博士生导师,主要从事智能优化与调度的研究.E-mail:lijunqing@lcu-cs.com;杜宇 男,1992年出生,山东泰安人.博士研究生,主要从事智能优化与调度的研究.E-mail:dyscupse@163.com
  • 基金资助:
    国家自然科学基金(No.61773192);山东省高等学校科技计划重点项目(No.J17KZ005)

An Artificial Bee Colony Algorithm for Flexible Job Shop Scheduling with Transportation Resource Constraints

LI Jun-qing1,2, DU Yu1, TIAN Jie1, DUAN Pei-yong1, PAN Quan-ke3,2   

  1. 1. School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong 250014, China;
    2. College of Computer Science, Liaocheng University, Liaocheng, Shandong 252059, China;
    3. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China
  • Received:2020-04-21 Revised:2020-07-06 Online:2021-02-25 Published:2021-02-25

摘要: 本文针对一类柔性作业车间调度问题,综合考虑运输资源约束、工件间准备时间约束等条件,以最小化最大完工时间和能耗为目标,提出了一种改进的人工蜂群优化算法.为求解该问题,算法采用二维向量编码,即调度向量记录工件的调度顺序,机床分配向量记录工件分配可用机床情况,解码过程充分考虑运输资源、工件间准备时间等约束条件.在局部搜索策略方面,提出了五种不同的调度邻域结构,并根据目标特点,设计了一种机床分配邻域结构.围绕人工蜂群算法的三个阶段,提出了不同的改进策略.为进一步提升算法的全局搜索能力,嵌入了模拟退火接受准则.实验结果验证了所提算法的优势显著.

 

关键词: 人工蜂群, 柔性作业车间, 能耗, 起重机运输, 准备时间

Abstract: The flexible job shop scheduling problem is investigated,where the transportation resource and operation related setup time constraints are considered simultaneously.The objective is to minimize the maximum completion time and the energy consumption.To solve the problem,we propose an improved artificial bee colony algorithm,where each solution is represented by a two-dimensional vector,the scheduling vector is to record the operation processing sequence,and the machine assignment vector is to assign the candidate machine for each operation.In the decoding mechanism,the transportation and setup time constraints are investigated.For the local search approaches,we develop five types of neighborhood structures for the scheduling part,and a well-designed machine assignment neighborhood structure for the machine assignment vector.To enhance the global searching abilities,the simulated annealing acceptance method is embedded.Finally,the experiment comparisons verify the performance of the proposed algorithm.

Key words: artificial bee colony, flexible job shop, energy consumption, crane transportation, setup time

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