多维最优同步态引导的“生产-物流”乱序联动管控机制与方法

doi:10.3901/JME.260297

机械工程学报 ›› 2026, Vol. 62 ›› Issue (11): 132-146.doi: 10.3901/JME.260297

• 特邀专栏：制造互联与工业智能 • 上一篇

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多维最优同步态引导的“生产-物流”乱序联动管控机制与方法

李明星^1,2,3, 刘斌洋², 罗启洁², 屈挺^1,2,3, 钟润阳⁴

1. 暨南大学广东省大湾区智慧物流国际科技合作基地珠海 519070;
2. 暨南大学智能科学与工程学院珠海 519070;
3. 暨南大学物联网与物流工程研究院珠海 519070;
4. 香港大学数据与系统工程系香港 999077

收稿日期:2025-06-02 修回日期:2025-12-20 发布日期:2026-07-29
作者简介:李明星,男,1996年出生,博士,副教授。主要研究方向为面向智能制造系统的生产-物流联动,AI+智能制造与智慧物流。E-mail:limingxing@jnu.edu.cn;刘斌洋,女,2001年出生,硕士。主要研究方向为面向智能制造系统的生产-物流联动。E-mail:liuby88@stu2023.jnu.edu.cn;罗启洁,女,2002年出生,硕士研究生。主要研究方向为面向智能制造系统的生产-物流联动。E-mail:1205907055@qq.com;屈挺(通信作者),男,1979年出生,博士,教授。主要研究方向为智能制造与智慧物流,供应链与产业链管理。E-mail:quting@jnu.edu.cn;钟润阳,男,1981年出生,博士,副教授。主要研究方向为智能制造与大数据分析,建筑工业化。E-mail:zhongzry@hku.hk
基金资助:
国家自然科学基金(52405548，52375498)、广东省基础与应用基础研究基金(2026A1515011558)、广东省哲学社会科学规划(GD25YSG14)、广东省普通高校特色创新(2024KTSCX009)和2019年度“广东特支计划”本土创新创业团队(2019BT02S593)资助项目。

Multi-dimensional Optimal Sync-state-guided Out-of-order Synchronization for Production-logistics Management and Control

LI Mingxing^1,2,3, LIU Binyang², LUO Qijie², QU Ting^1,2,3, ZHONG Runyang⁴

1. Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics, Jinan University, Zhuhai 519070;
2. School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070;
3. Institute of Physical Internet, Jinan University, Zhuhai 519070;
4. Department of Data and Systems Engineering, The University of Hong Kong, Hong Kong 999077

Received:2025-06-02 Revised:2025-12-20 Published:2026-07-29

摘要/Abstract

摘要： 订单驱动的定制生产趋势下，制造企业面临的外部需求种类、数量、时间、定制化程度等方面均高度随机，同时考虑不确定性扰动如紧急订单插入、设备故障等，生产与物流之间的动态衔接与同步是系统高效运作的关键。针对复杂不确定环境下柔性单元制造系统“生产-物流”协同问题，提出多维最优同步态引导的“生产-物流”乱序联动管控机制与方法。首先，受计算机处理器“乱序执行”启发，剖析处理器与制造系统运作映射关系，构建基于乱序执行的生产-物流运作指令化联动机制。其次，提出多维最优同步态引导的生产-物流乱序决策方法，构建面向多维度同步的生产-物流联动优化模型，并设计改进多目标模拟退火算法求解生成初始队列；接着以最优同步态为引导，通过实时数据驱动的多维优先级进行指令分发控制，实现随机扰动下生产-物流任务执行顺序与资源分配的动态调整，确保系统的实时性能与资源利用率。实验室案例仿真结果表明，所提方法显著提升了系统在复杂不确定场景中的运作效率及韧性，为智能制造动态管控与数据驱动的运作优化提供了创新视角与思路。

关键词: 智能制造, 生产调度, 不确定性, 多目标优化, 数据驱动决策

Abstract: Order-driven customized manufacturing enterprises face highly stochastic external demands with respect to variety, quantity, due time, and customization level. Meanwhile, disturbances within the manufacturing system such as the insertion of urgent orders and equipment failures, are inevitable. Consequently, the dynamic coordination and synchronization between production and logistics (PL) emerge as the key for the efficient operation of systems. A multi-dimensional optimal synchronization (SYNC) state-guided out-of-order synchronization for PL management and control is proposed, to address the collaborative optimization challenges confronted by flexible manufacturing systems in complex, uncertain environments. Firstly, inspired by the “Out-of-order execution (OoOE)” found in computer processors, the mapping relationship between processors and the operations of manufacturing systems is analyzed. An instruction-based OoOE synchronization mechanism for PL operations within smart manufacturing systems is developed. Subsequently, an out-of-order decision-making approach guided by a multi-dimensional optimal sync-state is designed. A PL optimization model focused on multi-dimensional synchronization is formulated and an improved multi-objective simulated annealing algorithm is designed to generate an initial instruction queue. Guided by the optimal sync-state, real-time data-driven multi-dimensional priority is designed to control instruction dispatch, facilitating dynamic adjustments to the execution order of PL operations and resource allocation in response to disturbances to ensure the real-time performance and resource utilization of the system. Experiment results from laboratory case studies demonstrate that the proposed method significantly enhances the efficiency and resilience of the system in complex uncertain scenarios, providing innovative insights and strategies for dynamic control and data-driven optimization within the realm of smart manufacturing.

Key words: smart manufacturing, production scheduling, uncertainty, multi-objective optimization, data-driven decisions

中图分类号:

TH166
TH186

李明星, 刘斌洋, 罗启洁, 屈挺, 钟润阳. 多维最优同步态引导的“生产-物流”乱序联动管控机制与方法[J]. 机械工程学报, 2026, 62(11): 132-146.

LI Mingxing, LIU Binyang, LUO Qijie, QU Ting, ZHONG Runyang. Multi-dimensional Optimal Sync-state-guided Out-of-order Synchronization for Production-logistics Management and Control[J]. Journal of Mechanical Engineering, 2026, 62(11): 132-146.

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http://www.cjmenet.com.cn/CN/Y2026/V62/I11/132

参考文献

[1] 周亚勤，肖蒙，吕志军，等. 基于DQN算法的考虑AGV小车搬运的离散制造车间调度方法[J]. 机械工程学报，2024，60(18)：338-348. ZHOU Yaqin，XIAO Meng，LÜ Zhijun，et al. Study on the discrete manufacturing workshop scheduling method based on DQN algorithm considering AGV[J]. Journal of Mechanical Engineering，2024，60(18)：338-348.
[2] LI Mingxing，GUO Daqiang，LI Ming，et al. Operation twins：Production-intralogistics synchronisation in Industry 4.0[J]. International Journal of Production Research，2022，61(15)，5193-5211.
[3] NIELSEN C P，AVHAD A，SCHOU C，et al. Control system architecture for matrix-structured manufacturing systems[J]. Computers in Industry，2023，146：103851.
[4] LENG Jiewu，CHEN Ziying，SHA Weinan，et al. Cloud-edge orchestration-based bi-level autonomous process control for mass individualization of rapid printed circuit boards prototyping services[J]. Journal of Manufacturing Systems，2022，63：143-161.
[5] 屈挺，张凯，闫勉，等. 物联网环境下面向高动态性生产系统优态运行的联动决策与控制方法[J]. 机械工程学报. 2018，54(16)：24-33. QU Ting，ZHANG Kai，YAN Mian，et al. Synchronized decision-making and control method for opti-state execution of dynamic production systems with internet of things[J]. Journal of Mechanical Engineering，2018，54(16)：24-33.
[6] 蔡磊，李文锋，罗云. 个性化定制车间生产-物流协同调度框架与算法研究[J]. 机械工程学报2022，58(7)：214-226. CAI Lei，LI Wenfeng，LUO Yun. Framework and Algorithm of customized workshop production-logistics collaborative scheduling[J]. Journal of Mechanical Engineering，2022，58(7)：214-226.
[7] GUO Daqiang，LI Mingxing，LYU Zhongyuan，et al. Synchroperation in Industry 4.0 manufacturing[J]. International Journal of Production Economics，2021，238：108171.
[8] ZHAO Chuang，WANG Shilong，YANG Bo，et al. A coupling optimization method of production scheduling and logistics planning for product processing-assembly workshops with multi-level job priority constraints[J]. Computers & Industrial Engineering，2024，190：110014.
[9] HAN Xiaoqing，CHENG Weiyao，MENG Leilei，et al. A dual population collaborative genetic algorithm for solving flexible job shop scheduling problem with AGV[J]. Swarm and Evolutionary Computation，2024，86：101538.
[10] YUAN Minghai，LI Yadong，PEI Fengque，et al. Dual-resource integrated scheduling method of AGV and machine in intelligent manufacturing job shop[J]. Journal of Central South University，2021，28(8)：2423-2435.
[11] WANG Lei，LUO Zhengda，TANG Hongtao，et al. A novel model for dynamic manufacturing service collaboration on industrial internet[J]. IEEE Transactions on Industrial Informatics，2023，19(12)：11788-11799.
[12] 白朝阳，张昊楠，苗琪琪，等. 基于GEP-DES的柔性流水车间机器与AGV集成实时调度方法[J]. 计算机集成制造系统，2023，29(12)：4161-4174. BAI Zhaoyang，ZHANG Haonan，MIAO Qiqi，et al. Integrated scheduling method of machines and automated guided vehicles for flexible flow shop in real-time systems based on GEP-DES algorithm[J]. Computer Integrated Manufacturing Systems，2023，29(12)：4161-4174.
[13] GHALEB M，ZOLFAGHARINIA H，TAGHIPOUR S. Real-time production scheduling in the Industry-4.0 context：Addressing uncertainties in job arrivals and machine breakdowns[J]. Computers & Operations Research，2020，123：105031.
[14] 张雷，张开. 面向紧急订单的汽车混流生产线调度优化[J]. 计算机集成制造系统，2026，32(1)：159-173. ZHANG Lei，ZHANG Kai. Scheduling optimization of automotive mixed flow lines for rush orders[J]. Computer Integrated Manufacturing Systems，2026，32(1)： 159-173.
[15] 王亚良，高康洪，范欣宇，等. 面向不确定性的车间布局调度集成建模与优化[J]. 机械工程学报，2023，59(5)：235-245. WANG Yaliang，GAO Kanghong，FAN Xinyu，et al. Uncertainty-oriented workshop layout and scheduling integration optimization[J]. Journal of Mechanical Engineering，2023，59(5)：235-245.
[16] KUSIAK A. Smart manufacturing must embrace big data[J]. Nature，2017，544：23-25.
[17] FENG Jiqiang，LI Feipeng，XU Chen，et al. Data-driven analysis for RFID-enabled smart factory：A case study[J]. IEEE Transactions on Systems，Man，and Cybernetics：Systems，2020，50(1)：81-88.
[18] FENG Jiqiang，LI Feipeng，XU Chen，et al. Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network[J]. Robotics and Computer-Integrated Manufacturing，2022，74：102283.
[19] 苑明海，李亚东，裴凤雀，等. 基于改进案例推理的智能车间扰动处理决策研究[J]. 中国机械工程，2021，32(20)：2458-2467. YUAN Minghai，LI Yadong，PEI Fengque，et al. Research on intelligent workshop disturbance processing decision by improved case-based reasoning[J]. China Mechanical Engineering，2021，32(20)：2458-2467.
[20] MAHMOODI E，FATHI M，TAVANA M，et al. Data-driven simulation-based decision support system for resource allocation in Industry 4.0 and smart manufacturing[J]. Journal of Manufacturing Systems，2024，72：287-307.

多维最优同步态引导的“生产-物流”乱序联动管控机制与方法

Multi-dimensional Optimal Sync-state-guided Out-of-order Synchronization for Production-logistics Management and Control

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