The Tourism College of Changchun University, Jilin Changchun 130607, China.
Changchun SCI-TECH University, Jilin Changchun 130600, China.
Comput Intell Neurosci. 2022 Apr 28;2022:6602545. doi: 10.1155/2022/6602545. eCollection 2022.
The present work expects to meet the personalized needs of the continuous development of various products and improve the joint operation of the intraenterprise Production and Distribution (P-D) process. Specifically, this paper studies the enterprise's P-D optimization. Firstly, the P-D linkage operation is analyzed under dynamic interference. Secondly, following a literature review on the difficulties and problems existing in the current P-D logistics linkage, the P-D logistics linkage-oriented decision-making information architecture is established based on Digital Twins. Digital Twins technology is mainly used to accurately map the P-D logistics linkage process's real-time data and dynamic virtual simulation. In addition, the information support foundation is constructed for P-D logistics linkage decision-making and collaborative operation. Thirdly, a Digital Twins-enabled P-D logistics linkage-oriented decision-making mechanism is designed and verified under the dynamic interference in the linkage process. Meanwhile, the lightweight deep learning algorithm is used to optimize the proposed P-D logistics linkage-oriented decision-making model, namely, the Collaborative Optimization (CO) method. Finally, the proposed P-D logistics linkage-oriented decision-making model is applied to a domestic Enterprise H. It is simulated by the Matlab platform using sensitivity analysis. The results show that the production, storage, distribution, punishment, and total costs of linkage operation are 24,943 RMB, 3,393 RMB, 2,167 RMB, 0 RMB, and 30,503 RMB, respectively. The results are 3.7% lower than the nonlinkage operation. The results of sensitivity analysis provide a high reference value for the scientific management of enterprises.
本研究旨在满足各种产品不断发展的个性化需求，并提高企业内生产与配送（Production and Distribution，P-D）过程的协同运作。具体而言，本文研究了企业的 P-D 优化问题。首先，分析了动态干扰下的 P-D 联动运作。其次，在回顾了当前 P-D 物流联动中存在的困难和问题的文献之后，基于数字孪生技术（Digital Twins）建立了面向 P-D 物流联动的决策信息架构。数字孪生技术主要用于准确映射 P-D 物流联动过程的实时数据和动态虚拟仿真，为 P-D 物流联动决策和协同运作构建信息支持基础。然后，设计并验证了在联动过程中动态干扰下基于数字孪生的 P-D 物流联动决策机制。同时，采用轻量级深度学习算法优化了所提出的面向 P-D 物流联动的决策模型，即协同优化（CO）方法。最后，将所提出的面向 P-D 物流联动的决策模型应用于国内企业 H，并通过 Matlab 平台进行仿真，通过灵敏度分析得出结果。结果表明，联动运作的生产、存储、配送、惩罚和总成本分别为 24943 元、3393 元、2167 元、0 元和 30503 元，比非联动运作降低了 3.7%。灵敏度分析的结果为企业的科学管理提供了高参考价值。
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