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The beer game is a widely used in-class game that is played in supply chain management classes to demonstrate the bullwhip effect.
Optimal policies for a multi-echelon inventory problem
A. J. Clark and H. Scarf · 1960
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A multi-echelon inventory model for a repairable item with one-for-one replenishment
S. C. Graves · 1985
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Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment
J. D. Sterman · 1989
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Self-improving reactive agents based on reinforcement learning, planning and teaching
L.-J. Lin · 1992
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Lower bounds for multi-echelon stochastic inventory systems
F. Chen and Y. Zheng · 1994
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Information distortion in a supply chain: The bullwhip effect
H. L. Lee, V. Padmanabhan, and S. Whang · 1997
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The dynamics of reinforcement learning in cooperative multiagent systems
C. Claus and C. Boutilier · 1998
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Reinforcement learning: An introduction
R. S. Sutton and A. G. Barto · 1998
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Stock positioning and performance estimation in serial production-transportation systems
G. Gallego and P. Zipkin · 1999
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The stationary beer game
F. Chen and R. Samroengraja · 2000
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The complexity of decentralized control of markov decision processes
D. S. Bernstein, R. Givan, N. Immerman, and S. Zilberstein · 2002
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Inventory management in supply chains: A reinforcement learning approach
I. Giannoccaro and P. Pontrandolfo · 2002
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Computers play the beer game: Can artificial agents manage supply chains?
S. O. Kimbrough, D.-J. Wu, and F. Zhong · 2002
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Comments on “Information distortion in a supply chain: The bullwhip effect”
H. L. Lee, V. Padmanabhan, and S. Whang · 2004
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Behavioral causes of the bullwhip effect and the observed value of inventory information
R. Croson and K. Donohue · 2006
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On bullwhip in supply chains—historical review, present practice and expected future impact
S. Geary, S. M. Disney, and D. R. Towill · 2006
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Beer game order policy optimization under changing customer demand
F. Strozzi, J. Bosch, and J. Zaldivar · 2007
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A reinforcement learning model for supply chain ordering management: An application to the beer game
S. K. Chaharsooghi, J. Heydari, and S. H. Zegordi · 2008
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Case-based reinforcement learning for dynamic inventory control in a multi-agent supply-chain system
Stock-out prediction in multi-echelon networks
A. Oroojlooyjadid, L. Snyder, and M. Takáč · 2017
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Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning
P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. Langlotz, K. Shpanskaya, et al · 2017
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Multi-echelon base-stock optimization with upstream stockout costs
L. V. Snyder · 2018
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The big data newsvendor: Practical insights from machine learning
G.-Y. Ban and C. Rudin · 2019
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Dynamic procurement of new products with covariate information: The residual tree method
G.-Y. Ban, J. Gallien, and A. J. Mersereau · 2019
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C. Jiang and Z. Sheng · 2009
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A survey on transfer learning
S. J. Pan and Q. Yang · 2010
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The Beer Game: Its History and Rule Changes
I. J. Martinez-Moyano, J. Rahn, and R. Spencer · 2014
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al · 2015
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Foodmart’s database tables
Pentaho · 2015
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Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
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The AI that has nothing to learn from humans
D. Chan · 2017
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Can deep reinforcement learning improve inventory management? performance on dual sourcing, lost sales and multi-echelon problems
J. Gijsbrechts, R. N. Boute, J. A. Van Mieghem, and D. Zhang · 2019
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Store item demand forecasting challenge
Kaggle.com · 2019
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A review of cooperative multi-agent deep reinforcement learning
A. OroojlooyJadid and D. Hajinezhad · 2019
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Fundamentals of Supply Chain Theory
L. V. Snyder and Z.-J. M. Shen · 2019
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A theoretical analysis of deep q-learning
Z. Yang, Y. Xie, and Z. Wang · 2019
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Multi-agent reinforcement learning: A selective overview of theories and algorithms
K. Zhang, Z. Yang, and T. Başar · 2019
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Applying deep learning to the newsvendor problem
A. Oroojlooyjadid, L. V. Snyder, and M. Takáč · 2020
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