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Deep Neural Newsvendor
16 references·0 citing·42 second hopsampled
Deep Neural Newsvendor (2023)Deep Neural NewsvendorHow Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time (2023)How Big Should Your Data Real…Stochastic Optimization Forests (2022)Stochastic Optimization Fores…Can Deep Reinforcement Learning Improve Inventory Management? Performance on Lost Sales, Dual-Sourcing, and Multi-Echelon Problems (2022)Can Deep Reinforcement Learni…Product Choice with Large Assortments: A Scalable Deep-Learning Model (2021)Product Choice with Large Ass…A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization (2021)A Deep Q-Network for the Beer…A Practical End-to-End Inventory Management Model with Deep Learning (2020)A Practical End-to-End Invent…From Predictive to Prescriptive Analytics (2019)From Predictive to Prescripti…Applying deep learning to the newsvendor problem (2019)Applying deep learning to the…The Big Data Newsvendor: Practical Insights from Machine Learning (2018)The Big Data Newsvendor: Prac…Technical Note—Perishable Inventory Systems: Convexity Results for Base-Stock Policies and Learning Algorithms Under Censored Demand (2018)Technical Note—Perishable Inv…How Research in Production and Operations Management May Evolve in the Era of Big Data (2017)How Research in Production an…Technical Note—Nonparametric Data-Driven Algorithms for Multiproduct Inventory Systems with Censored Demand (2016)Technical Note—Nonparametric …The Data-Driven Newsvendor Problem: New Bounds and Insights (2015)The Data-Driven Newsvendor Pr…Lectures on Stochastic Programming (2009)Lectures on Stochastic Progra…Provably Near-Optimal Sampling-Based Policies for Stochastic Inventory Control Models (2007)Provably Near-Optimal Samplin…A practical inventory control policy using operational statistics (2004)A practical inventory control…A Stochastic Approximation Method (1951)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Optimization of conditional value-at-risk (2000)Impact of business analytics and enterprise systems on managerial accounting (2017)Smart “Predict, then Optimize” (2021)Frameworks and Results in Distributionally Robust Optimization (2022)Foundations of Inventory Management (2000)The Sample Average Approximation Method for Stochastic Discrete Optimization (2002)Stochastic Simulation (2007)Distributionally Robust Convex Optimization (2014)Distributionally Robust Stochastic Optimization with Wasserstein Distance (2022)The Distribution Free Newsboy Problem: Review and Extensions (1993)Inventory management and production planning and scheduling (1999)A Min-Max Solution of an Inventory Problem (2005)Monte Carlo sampling-based methods for stochastic optimization (2014)Quantitative Models for Supply Chain Management (1999)Regret in the Newsvendor Model with Partial Information (2008)Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint (2010)Servitization of business: Adding value by adding services (1988)The Theory and Practice of Revenue Management (2004)Predicting the Present with Google Trends (2012)Forecasting sales in the supply chain: Consumer analytics in the big data era (2018)Research in Operations Management and Information Systems Interface (2018)Impact of cybersecurity on operations and supply chain management: Emerging trends and future research directions (2022)Studies in Mathematical Theory of Inventory and Production (1959)Judgment under Uncertainty: Heuristics and Biases (1975)Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment (1989)Information Distortion in a Supply Chain: The Bullwhip Effect (1997)Deep reinforcement learning for inventory control: A roadmap (2021)The role of artificial intelligence in supply chain management: mapping the territory (2022)Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation (2024)Optimal Policies for a Multi-Echelon Inventory Problem (1960)Approximations of Dynamic, Multilocation Production and Inventory Problems (1984)Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature (2023)Deep Reinforcement Learning for inventory optimization with non-stationary uncertain demand (2023)MACHINE LEARNING'S INFLUENCE ON SUPPLY CHAIN AND LOGISTICS OPTIMIZATION IN THE OIL AND GAS SECTOR: A COMPREHENSIVE ANALYSIS (2024)Coherent Measures of Risk (1999)Data-driven chance constrained stochastic program (2015)Theory of games and economic behavior (2019)Strengthening supply chain resilience during COVID ‐19: A case study of JD .com (2021)A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality (2023)A survey of contextual optimization methods for decision-making under uncertainty (2024)
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References

  • 2009Lectures on Stochastic Programming2,556 cited
  • 2019From Predictive to Prescriptive Analytics868 cited
  • 2018The Big Data Newsvendor: Practical Insights from Machine Learning531 cited
  • 2021A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization322 cited
  • 2007Provably Near-Optimal Sampling-Based Policies for Stochastic Inventory Control Models261 cited
  • 2015The Data-Driven Newsvendor Problem: New Bounds and Insights260 cited
  • 2017How Research in Production and Operations Management May Evolve in the Era of Big Data243 cited
  • 2004A practical inventory control policy using operational statistics216 cited
  • 2022Can Deep Reinforcement Learning Improve Inventory Management? Performance on Lost Sales, Dual-Sourcing, and Multi-Echelon Problems206 cited
  • 2020A Practical End-to-End Inventory Management Model with Deep Learning178 cited
  • 2019Applying deep learning to the newsvendor problem163 cited
  • 2016Technical Note—Nonparametric Data-Driven Algorithms for Multiproduct Inventory Systems with Censored Demand84 cited
  • 2021Product Choice with Large Assortments: A Scalable Deep-Learning Model77 cited
  • 2018Technical Note—Perishable Inventory Systems: Convexity Results for Base-Stock Policies and Learning Algorithms Under Censored Demand

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74 cited
  • 2023How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time70 cited
  • 2022Stochastic Optimization Forests51 cited
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