引用本文:孙美玲,顾幸生.改进生物地理学优化算法求解模糊分布式柔性作业车间调度问题[J].控制理论与应用,2025,42(4):713~721.[点击复制]
SUN Mei-ling,GU Xing-sheng.An improved biogeography-based optimization algorithm for fuzzy distributed flexible job-shop scheduling problem[J].Control Theory & Applications,2025,42(4):713~721.[点击复制]
改进生物地理学优化算法求解模糊分布式柔性作业车间调度问题
An improved biogeography-based optimization algorithm for fuzzy distributed flexible job-shop scheduling problem
摘要点击 0  全文点击 0  投稿时间：2023-06-07  修订日期：2025-03-08
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DOI编号  10.7641/CTA.2023.30395
  2025,42(4):713-721
中文关键词  生产调度  模糊分布式  柔性作业车间  生物地理学优化算法  调度规则  模拟退火
英文关键词  production scheduling  fuzzy distributed  flexible job-shop  biogeography-based optimization algorithm  scheduling rule  simulated annealing
基金项目  国家自然科学基金项目(61973120,62076095)资助.
作者单位E-mail
孙美玲 华东理工大学能源化工过程智能制造教育部重点实验室 13061260501@163.com 
顾幸生* 华东理工大学能源化工过程智能制造教育部重点实验室 xsgu@ecust.edu.cn 
中文摘要
      经济全球化推动制造企业从单一工厂向多工厂协同模式转变,模糊分布式柔性作业车间调度问题(FDFJ SP)成为调度领域的研究热点.为最小化FDFJSP的最大模糊完工时间,本文提出了一种基于模拟退火和局部搜索策 略的生物地理学优化算法(BBOSL).根据问题特点,设计了工厂–随机键的新型编解码方案;通过调度规则生成半数 初始种群以提高种群质量;提出了基于模拟退火算法的新解接受方法和基于关键工厂的局部搜索策略以增强搜索 能力;通过对算法参数调优提升了算法性能.实验结果验证了改进策略的有效性,并与现有算法进行了对比实验,验 证了其在模糊集中式和模糊分布式柔性作业车间调度问题上的优越性.
英文摘要
      The globalization of the economy has prompted manufacturing enterprises to transition from a single factory to a multi-factory collaborative model, making the fuzzy distributed flexible job-shop scheduling problem (FDFJSP) a re search hotspot in the scheduling field. In this paper, a novel biogeography-based optimization algorithm based on simulated annealing and local search strategy (BBOSL) is proposed to minimize the maximum fuzzy completion time of FDFJSP. Based onthecharacteristics of the problem, a new factory-random key encoding and decoding scheme is designed. Scheduling rules are used to generate half of the initial population to improve the population quality. A new solution acceptance method based on a simulated annealing algorithm and a local search strategy based on a critical factory are proposed to enhance the search capability. The algorithm parameters are tuned to improve algorithm performance. The experimental re sults validate the effectiveness of the improved strategy and compare it with the existing algorithms to verify its superiority in fuzzy centralized and fuzzy distributed flexible job-shop scheduling problems.