Application of Reinforcement Learning Algorithm in Delivery Order System under Supply Chain Environment

Abstract

With the intensification of market competition and the development of market globalization, the efficiency of supply chain management orders has become an important part of enterprise competition resources. The competition among enterprises is fierce. To achieve effective customer response quickly, the time for supply chain order management is minimized, and refine the order processing process. This article introduces the strategy research of supply chain management order based on a reinforcement learning algorithm. This article first combines the reinforcement learning algorithm and deep learning algorithm, using the optimal decision-making ability of reinforcement learning algorithm and deep learning algorithm. The combination of data perception and the optimal ability to analyze examine the data of the order process, order cycle, and order delivery process of the supply chain order management and give the optimal decision. The supply chain order management process conducts questionnaire surveys and seminars to understand the current process of supply chain order management and the problems derived from the analysis of data based on the deep learning algorithm. Finally, through the output of the optimal strategy of the reinforcement learning algorithm, the supply chain order management process was improved, and the satisfaction survey was conducted again. The survey showed that the satisfaction was improved, and the satisfaction reached more than 90%.

Integration of machine learning in the supply chain for decision making: A systematic literature review

“…Furthermore, the integration of reinforcement learning with deep learning algorithms for supply chain order management is addressed by Huang and Tan (2021) whose model allows determining when and how much to order to minimize total inventory costs, considering factors such as storage costs, transportation costs, and uncertain product demand. In contrast, Andaur, Ruz and Goycoolea ( 2021 2020) approach is based on the fact that, instead of making assumptions about demand distribution, which can often be incorrect, the machine learning model uses historical demand data and independent features to predict future demand.…”

Research on Optimization Strategies for Closed-Loop Supply Chain Management Based on Deep Learning Technology

“…For this reason, Guan and Yu (2021) designed a supply chain resource distribution allocation model based on deep learning. Huang and Tan (2021) introduced the strategy research of supply chain management order based on a reinforcement learning algorithm. The supply chain order management process involves conducting questionnaire surveys and seminars to understand the current process of supply chain order management and the problems derived from the analysis of data based on the deep learning algorithm.…”