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This work provides a Deep Reinforcement Learning approach to solving a periodic review inventory control system with stochastic vendor lead times, lost sales, correlated demand, and price matching.
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Improved Forecast Accuracy in Airline Revenue Management by Unconstraining Demand Estimates from Censored Data
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Lost-sales problems with stochastic lead times: Convexity results for base-stock policies
G. Janakiraman and R. O. Roundy · 2004
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A new and simple policy for the continuous review lost sales inventory model
M. I. Reiman · 2004
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Out-of-stock: reactions, antecedents, management solutions, and a future perspective
P. C. Verhoef and L. M. Sloot · 2006
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A newsvendor’s procurement problem when suppliers are unreliable
M. Dada, N. C. Petruzzi, and L. B. Schwarz · 2007
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P. Zipkin · 2008
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W. T. Huh, G. Janakiraman, J. A. Muckstadt, and P. Rusmevichientong · 2009
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Sample path generation for probabilistic demand forecasting
D. Madeka, L. Swiniarski, D. Foster, L. Razoumov, K. Torkkola, and R. Wen · 2018
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Simple random search provides a competitive approach to reinforcement learning
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Model Predictive Control: Theory, Computation, and Design
J. B. Rawlings, D. Q. Mayne, and M. M. Diehl · 2018
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R. S. Sutton and A. G. Barto · 2018
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A. Agarwal, N. Jiang, S. M. Kakade, and W. Sun · 2019
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Orl: Reinforcement learning benchmarks for online stochastic optimization problems
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On implications of demand censoring in the newsvendor problem
O. Besbes and A. Muharremoglu · 2013
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Price matching guarantees and consumer search
M. C. Janssen and A. Parakhonyak · 2013
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G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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A practical end-to-end inventory management model with deep learning
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Can deep reinforcement learning improve inventory management? performance on dual sourcing, lost sales and multi-echelon problems
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