Global supply chain management: A reinforcement learning approach
Global supply chain management: A reinforcement learning approach
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全球供应链管理：强化学习方法
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
T. Das
中科院分区：
文献类型：
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作者：
P. Pontrandolfo;A. Gosavi;O. Okogbaa;T. Das
近年来，研究者和实践者都投入了大量的关注供应链管理（SCM）。供应链管理的主要焦点是需要整合沿着供应链的运作，作为整体后勤支持功能的一部分。与此同时，全球化的需要要求在国际范围内解决供应链管理问题，作为我们所说的全球供应链管理（GSCM）的一部分。本文提出了一种方法来研究GSCM问题，使用人工智能框架称为强化学习（RL）。RL框架允许在集成视角下管理全球供应链。RL方法与自主代理网络（AAN）有着显著的相似性;我们将讨论这种相似性。RL方法应用于一个案例，即一个网络化的生产系统，跨越几个地理区域和物流阶段。我们讨论的结果，并提供指导方针和实际应用的影响。
In recent years, researchers and practitioners alike have devoted a great deal of attention to supply chain management (SCM). The main focus of SCM is the need to integrate operations along the supply chain as part of an overall logistic support function. At the same time, the need for globalization requires that the solution of SCM problems be performed in an international context as part of what we refer to as Global Supply Chain Management (GSCM). This paper proposes an approach to study GSCM problems using an artificial intelligence framework called reinforcement learning (RL). The RL framework allows the management of global supply chains under an integration perspective. The RL approach has remarkable similarities to that of an autonomous agent network (AAN); a similarity that we shall discuss. The RL approach is applied to a case example, namely a networked production system that spans several geographic areas and logistics stages. We discuss the results and provide guidelines and implications for practical applications.