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<p>The use of deep reinforcement learning algorithms for strategy formulation in supply chain management enables the nodes in the supply chain to better improve their management strategies. In this paper, a supply chain model is constructed as a starting point, and deep reinforcement learning algorithms are introduced on this basis. Firstly, the decision problem of uncertainty is handled by the reinforcement learning method of functions, and the DQN algorithm (deep neural network algorithm) is divided into two parts for iterative rules. Then the target network is established to make the iterative process more stable, to improve the convergence of the algorithm, evaluate the loss function in the training process of the network, and to determine its influence factor. Then the neural network is used to improve the iteration rule, improve the output layer, select the final action, and define the model expectation reward. Finally, the Bellman equation is fitted to the function by a deep neural network to calculate the final result. The experimental results show that by analyzing and constructing the cost of international logistics under supply chain management, the capacity utilization rate of ocean freight link is 57% The unloading link is 74% and the total capacity utilization rate is calculated as 76%. It shows that using deep reinforcement learning algorithms under international logistics supply chain management is feasible and necessary for improving the management strategy research of supply chains.</p>
</abstract>

The use of deep reinforcement learning algorithms for strategy formulation in supply chain management enables the nodes in the supply chain to better improve their management strategies. In this paper, a supply chain model is constructed as a starting point, and deep reinforcement learning algorithms are introduced on this basis. Firstly, the decision problem of uncertainty is handled by the reinforcement learning method of functions, and the DQN algorithm (deep neural network algorithm) is divided into two parts for iterative rules. Then the target network is established to make the iterative process more stable, to improve the convergence of the algorithm, evaluate the loss function in the training process of the network, and to determine its influence factor. Then the neural network is used to improve the iteration rule, improve the output layer, select the final action, and define the model expectation reward. Finally, the Bellman equation is fitted to the function by a deep neural network to calculate the final result. The experimental results show that by analyzing and constructing the cost of international logistics under supply chain management, the capacity utilization rate of ocean freight link is 57% The unloading link is 74% and the total capacity utilization rate is calculated as 76%. It shows that using deep reinforcement learning algorithms under international logistics supply chain management is feasible and necessary for improving the management strategy research of supply chains.

Research on international logistics supply chain management strategy based on deep reinforcement learning

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The use of deep reinforcement learning algorithms for strategy formulation in supply chain management enables the nodes in the supply chain to better improve...

Research on international logistics supply chain management strategy based on deep reinforcement learning

Publicado en línea: 30 jun 2023

Páginas: -

Recibido: 08 oct 2022

Aceptado: 09 ene 2023

DOI: https://doi.org/10.2478/amns.2023.1.00474

Palabras clave
Deep reinforcement learning algorithm, Supply chain management, DQN algorithm, Impact factor, Bellman equation

© 2023 Yuzhen Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on international logistics supply chain management strategy based on deep reinforcement learning

Publicado en línea: 30 jun 2023

Páginas: -

Recibido: 08 oct 2022

Aceptado: 09 ene 2023

DOI: https://doi.org/10.2478/amns.2023.1.00474

Palabras claveDeep reinforcement learning algorithm, Supply chain management, DQN algorithm, Impact factor, Bellman equation

© 2023 Yuzhen Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Deep reinforcement learning algorithm, Supply chain management, DQN algorithm, Impact factor, Bellman equation