Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/287043 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Annals of Operations Research [ISSN:] 1572-9338 [Volume:] 308 [Issue:] 1-2 [Publisher:] Springer US [Place:] New York, NY [Year:] 2021 [Pages:] 415-439
Verlag: 
Springer US, New York, NY
Zusammenfassung: 
The coordination of order policies constitutes a great challenge in supply chain inventory management as various stochastic factors increase its complexity. Therefore, analytical approaches to determine a policy that minimises overall inventory costs are only suitable to a limited extent. In contrast, we adopt a heuristic approach, from the domain of artificial intelligence (AI), namely, Monte Carlo tree search (MCTS). To the best of our knowledge, MCTS has neither been applied to supply chain inventory management before nor is it yet widely disseminated in other branches of operations research. We develop an offline model as well as an online model which bases decisions on real-time data. For demonstration purposes, we consider a supply chain structure similar to the classical beer game with four actors and both stochastic demand and lead times. We demonstrate that both the offline and the online MCTS models perform better than other previously adopted AI-based approaches. Furthermore, we provide evidence that a dynamic order policy determined by MCTS eliminates the bullwhip effect.
Schlagwörter: 
Monte Carlo tree search
Supply chain inventory management
Artificial intelligence
Bullwhip effect
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