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- W4308067623 abstract "Decision-making in supply chains is challenged by high complexity, a combination of continuous and discrete processes, integrated and interdependent operations, dynamics, and adaptability. The rapidly increasing data availability, computing power and intelligent algorithms unveil new potentials in adaptive data-driven decision-making. Reinforcement Learning, a class of machine learning algorithms, is one of the data-driven methods. This semi-systematic literature review explores the current state of the art of reinforcement learning in supply chain management (SCM) and proposes a classification framework. The framework classifies academic papers based on supply chain drivers, algorithms, data sources, and industrial sectors. The conducted review revealed a few critical insights. First, the classic Q-learning algorithm is still the most popular one. Second, inventory management is the most common application of reinforcement learning in supply chains, as it is a pivotal element of supply chain synchronisation. Last, most reviewed papers address toy-like SCM problems driven by artificial data. Therefore, shifting to industry-scale problems will be a crucial challenge in the next years. If this shift is successful, the vision of data-driven decision-making in real-time could become a reality." @default.
- W4308067623 created "2022-11-08" @default.
- W4308067623 creator A5000645251 @default.
- W4308067623 creator A5014441161 @default.
- W4308067623 creator A5025644294 @default.
- W4308067623 creator A5035485597 @default.
- W4308067623 creator A5077154644 @default.
- W4308067623 creator A5085640653 @default.
- W4308067623 date "2022-11-03" @default.
- W4308067623 modified "2023-10-07" @default.
- W4308067623 title "A review on reinforcement learning algorithms and applications in supply chain management" @default.
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