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Reinforcement learning (RL) has been widely applied to game-playing and surpassed the best human-level performance in many domains, yet there are few use-cases in industrial or commercial settings.
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems
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PORTFOLIO SELECTION
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Discrete-Variable Extremum Problems
G. B. Dantzig · 1957
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A Linear Programming Approach to the Cutting-Stock Problem
P. C. Gilmore and R. E. Gomory · 1961
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An efficient method for finding the minimum of a function of several variables without calculating derivatives
M. J. D. Powell · 1964
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Approximation algorithms for combinatorial problems
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Shelf Algorithms for Two-Dimensional Packing Problems
B. S. Baker and J. S. Schwarz · 1983
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Large-scale portfolio optimization
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Stability of on-line bin packing with random arrivals and long-run-average constraints
C. Courcoubetis and R. Weber · 1990
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Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market
H. Konno and H. Yamazaki · 1991
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Global portfolio optimization
F. Black and R. Litterman · 1992
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The pallet loading problem: A survey
B. Ram · 1992
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Multi-stage stochastic linear programs for portfolio optimization
G. B. Dantzig and G. Infanger · 1993
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Sensitivity analysis for base-stock levels in multiechelon production-inventory systems
P. Glasserman and S. Tayur · 1995
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Average-case analysis of off-line and on-line knapsack problems
G. S. Lueker · 1995
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Stochastic on-line knapsack problems
A. Marchetti-Spaccamela and C. Vercellis · 1995
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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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Optimal policies and simulation-based optimization for capacitated production inventory systems
R. Kapuscinski and S. Tayur · 1999
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Robust modeling of multi-stage portfolio problems
A. Ben-Tal, T. Margalit, and A. Nemirovski · 2000
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Portfolio optimization with conditional value-at-risk objective and constraints
P. Krokhmal, J. Palmquist, and S. Uryasev · 2003
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Knapsack Problems , volume 53
H. Kellerer, U. Pferschy, and D. Pisinger · 2004
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A robust optimization approach to inventory theory
D. Bertsimas and A. Thiele · 2006
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Chapter 20: Robust Optimization Models in Finance
G. Cornuejols and R. Tutuncu · 2006
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On the Sum-of-Squares algorithm for bin packing
J. Csirik, D. S. Johnson, C. Kenyon, J. B. Orlin, P. W. Shor, and R. R. Weber · 2006
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G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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Approximation and online algorithms for multidimensional bin packing: A survey
H. I. Christensen, A. Khan, S. Pokutta, and P. Tetali · 2016
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A comprehensive survey of guaranteed-service models for multi-echelon inventory optimization, 2 2016
A. S. Eruguz, E. Sahin, Z. Jemai, and Y. Dallery · 2016
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High-dimensional continuous control using generalized advantage estimation
J. Schulman, P. Moritz, S. Levine, M. I. Jordan, and P. Abbeel · 2016
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Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms
E. Cortez, A. Bonde, A. Muzio, M. Russinovich, M. Fontoura, and R. Bianchini · 2017
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Numerical optimization
J. Nocedal and S. Wright · 2006
Cited alongside, same era.
Robust Portfolio Optimization and Management
F. J. Fabozzi, P. N. Kolm, D. A. Pachamanova, and S. M. Focardi · 2007
Cited alongside, same era.
Robust Optimization
A. Ben-Tal, L. El Ghaoui, and A. Nemirovsky · 2009
Cited alongside, same era.
A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms
V. DeMiguel, L. Garlappi, F. J. Nogales, and R. Uppal · 2009
Cited alongside, same era.
A Reinforcement Learning Approach for the Flexible Job Shop Scheduling Problem
Y. Martinez, A. Nowe, J. Suarez, and R. Bello · 2011
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V. Gupta and A. Radovanovic · 2012
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Pyomo — Optimization Modeling in Python
W. E. Hart, C. D. Laird, D. L. Woodruff, G. A. Hackebeil, B. L. Nicholson, and J. D. Siirola · 2017
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Deep Reinforcement Learning: An Overview
Y. Li · 2017
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A. Oroojlooyjadid, M. Nazari, L. Snyder, and M. Takáč · 2017
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Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. Lillicrap, K. Simonyan, and D. Hassabis · 2017
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Gurobi, 2018
Gurobi Optimization LLC · 2018
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Ray: A Distributed Framework for Emerging AI Applications
P. Moritz, R. Nishihara, S. Wang, A. Tumanov, R. Liaw, E. Liang, M. Elibol, Z. Yang, W. Paul, M. I. Jordan, and I. Stoica · 2018
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Reinforcement Learning: An Introduction
R. Sutton and A. Barto · 2018
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Dota 2 with Large Scale Deep Reinforcement Learning
C. Berner, G. Brockman, B. Chan, V. Cheung, C. Dennison, D. Farhi, Q. Fischer, S. Hashme, C. Hesse, R. Józefowicz, S. Gray, C. Olsson, J. Pachocki, M. Petrov, H. Pondé de Oliveira Pinto, J. Raiman, T. Salimans, J. Schlatter, J. Schneider, S. Sidor, I. Sutskever, J. Tang, F. Wolski, and S. Zhang · 2019
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A new dog learns old tricks: RL finds Classic optimization algorithms
W. Kong, D. Sivakumar, C. Liaw, and A. Mehta · 2019
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Attention, learn to solve routing problems!
W. Kool, H. Van Hoof, and M. Welling · 2019
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A Competitive Analysis of Online Knapsack Problems with Unit Density
W. Ma, D. Simchi-Levi, and J. Zhao · 2019
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BARON 19.7.13: Global Optimization of Mixed-Integer Nonlinear Programs, 2019
N. V. Sahinidis · 2019
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A deep reinforcement learning approach for chemical production scheduling
C. D. Hubbs, C. Li, N. V. Sahinidis, I. E. Grossmann, and J. M. Wassick · 2020
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