| 引用本文: | 王晶晶,郭琦,韩红桂.数模双驱的分布式生产物料供应智能优化调度[J].控制理论与应用,2025,42(11):2221~2230.[点击复制] |
| WANG Jing-jing,GUO Qi,HAN Hong-gui.Data-model driven intelligent optimization scheduling for distributed production material supply[J].Control Theory & Applications,2025,42(11):2221~2230.[点击复制] |
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| 数模双驱的分布式生产物料供应智能优化调度 |
| Data-model driven intelligent optimization scheduling for distributed production material supply |
| 摘要点击 2707 全文点击 134 投稿时间：2025-06-27 修订日期：2025-10-13 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2025.50266 |
| 2025,42(11):2221-2230 |
| 中文关键词 数模双驱 分布式生产 物料供应调度 智能优化 |
| 英文关键词 data-model driven distributed production material supply scheduling intelligent optimization |
| 基金项目 国家重点研发计划项目(2023YFB3308002),国家自然科学基金项目(62303028,62273193,92467205)资助. |
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| 中文摘要 |
| 随着制造企业间的合作日益普遍,分布式制造具有资源配置优化共享的优势,已成为一种现代化生产模式.
物料作为生产制造的核心资源, 供应商与生产商间的高效物料供应调度能够提升分布式生产效率、降低生产成
本. 本研究针对分布式生产物料供应调度问题,提出一种数模双驱的协智能优化方法, 同时优化生产商的满意度和
物料拖期目标.首先,针对多仓库、多工厂、多物料类型组成的复杂供应网络,构建考虑动态补货机制的混合整数规
划模型;其次,在中小规模和大规模问题上分别利用数学求解器Gurobi和启发式规则最大化满意度和最小化拖期并
得到两个单目标最优解,作为多目标优化的高质量起点和终点;然后,设计基于模型优化解的自适应路径重连机
制, 通过差异驱动自适应探索策略,提升多目标优化解集多样性;最后,提出目标驱动的局部增强搜索策略,进一步
提升算法性能.不同规模数据集的仿真实验结果表明了所提算法能够有效求解分布式物料供应调度问题. |
| 英文摘要 |
| With the increasing prevalence of cooperation among manufacturing enterprises, distributed manufacturing
characterized by the optimal sharing of resources has emerged as a modern production paradigm. As a core resource in
production manufacturing, efficient scheduling of material supply between suppliers and manufacturers can significantly
enhance distributed production efficiency and reduce production costs. To addresse the distributed production material
supply scheduling problem, a data-model driven intelligent optimization approach is proposed to simultaneously optimize
both manufacturer satisfaction and material tardiness. Firstly, a mixed-integer programming model incorporating a dy
namic replenishment mechanism is formulated for the complex supply network comprising multiple warehouses, factories,
and material types. Secondly, the mathematical solver Gurobi and heuristic rules are employed respectively to maximize
satisfaction and minimize tardiness for the small-scaled problems and large-scaled problems. Thus, two high-quality single
objective optimal solutions are yielded as the start and end points for multi-objective optimization. Thirdly, an adaptive
path-relinking mechanism is designed based on initial solutions, utilizing a difference-driven adaptive exploration strategy
to enhance diversity of the multi-objective solutions. Finally, a goal-driven local intensification is proposed to further im
prove exploitation. Experimental results on the instances with varying scales demonstrate that the proposed algorithm can
effectively solve the distributed material supply scheduling problem. |
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