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

doi:10.3901/JME.260297

Abstract

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

CLC Number:

TH166
TH186

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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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260297

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

References

[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.