| 引用本文: | 王凌,吴玉婷,陈靖方,潘子肖.Seru系统调度优化的知识引导协同进化算法[J].控制理论与应用,2024,41(6):959~966.[点击复制] |
| WANG Ling,WU Yu-ting,CHEN Jing-fang,PAN Zi-xiao.A knowledge-guided cooperative coevolutionary algorithm for Seru system scheduling optimization[J].Control Theory and Technology,2024,41(6):959~966.[点击复制] |
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| Seru系统调度优化的知识引导协同进化算法 |
| A knowledge-guided cooperative coevolutionary algorithm for Seru system scheduling optimization |
| 摘要点击 763 全文点击 192 投稿时间：2022-07-14 修订日期：2023-11-04 |
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| DOI编号 DOI: 10.7641/CTA.2023.20626 |
| 2024,41(6):959-966 |
| 中文关键词 赛汝(Seru)生产系统 协同搜索 知识驱动 增强搜索 调整策略 |
| 英文关键词 Seru production system cooperative search knowledge driven enhanced search adjustment strategy |
| 基金项目 国家自然科学基金项目(62273193)资助. |
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| 中文摘要 |
| 作为一种新型的生产模式, Seru系统能够兼顾柔性和效率且快速响应市场, 已在装配企业得到广泛应用.为了实现实际生产过程生产效率和劳动效率的协同优化, 本文研究以最小化最大完工时间和工人总劳动时间为目标的Seru系统多目标调度问题, 提出一种知识引导的协同进化算法. 首先, 将问题分解为Seru构造和Seru调度, 构造两个种群分别优化子问题. 同时, 设计种群规模的调整策略, 通过为有潜力的种群分配更多个体来提高协同搜索的效率. 进而, 通过分析问题的性质, 提炼规则性知识用于设计有效的搜索算子和重生成规则, 指导精英个体执行知识驱动的增强搜索, 从而进一步提升算法的局部开发能力. 通过数值仿真和统计性能对比, 验证了算法各设计环节的有效性, 并取得了显著优于现有最新算法的多目标调度优化性能. |
| 英文摘要 |
| As a new production mode, Seru system has been widely used in assembly enterprises due to its flexibility, efficiency, and fast response to the market. To optimize the production and labor efficiency simultaneously, this paper investigates the multi-objective scheduling problem of Seru system to minimize the makespan and the total labor time, and a knowledge-guided cooperative coevolutionary algorithm (KCCA) is proposed to solve this problem. First, the problem can be decomposed into two subproblems: Seru formation and Seru scheduling, and two populations are constructed to solve the two subproblems respectively. Meanwhile, to improve the search efficiency, the population size adjustment strategy is designed to allocate more individuals to the more potential population. Moreover, to further enhance the exploitation capability of KCCA, the knowledge is derived by analyzing the problem properties to design effective search operators and rules, which are used to perform the knowledge-guided enhanced search on elite individuals. Computational experiments and statistical comparisons validate the effectiveness of the specific designs of the KCCA, which can achieve a better optimization performance for multi-objective scheduling than state-of-the-art algorithms. |
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