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Learning Newsvendor Problems With Intertemporal Dependence and Moderate Non-stationarities
16 references·0 citing·44 second hopsampled
Learning Newsvendor Problems With Intertemporal Dependence and Moderate Non-stationarities (2020)Learning Newsvendor Problems With Interte…How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time (2023)How Big Should Your Data Real…An integrated data-driven method using deep learning for a newsvendor problem with unobservable features (2022)An integrated data-driven met…Smart “Predict, then Optimize” (2021)Smart “Predict, then Optimize”Distributionally Robust Conditional Quantile Prediction with Fixed Design (2021)Distributionally Robust Condi…A Practical End-to-End Inventory Management Model with Deep Learning (2020)A Practical End-to-End Invent…Quantile forecasting and data-driven inventory management under nonstationary demand (2019)Quantile forecasting and data…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…The Data-Driven Newsvendor Problem: New Bounds and Insights (2015)The Data-Driven Newsvendor Pr…Robust newsvendor problem with autoregressive demand (2015)Robust newsvendor problem wit…Safety stock planning under causal demand forecasting (2011)Safety stock planning under c…Solving operational statistics via a Bayesian analysis (2007)Solving operational statistic…Provably Near-Optimal Sampling-Based Policies for Stochastic Inventory Control Models (2007)Provably Near-Optimal Samplin…Introduction to Time Series and Forecasting (2002)Introduction to Time Series a…Testing the null hypothesis of stationarity against the alternative of a unit root (1992)Testing the null hypothesis o…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)Frameworks and Results in Distributionally Robust Optimization (2022)Distribution of the Estimators for Autoregressive Time Series With a Unit Root (1979)Distribution of the Estimators for Autoregressive Time Series With a Unit Root (1979)Testing for a Unit Root in Time Series Regression (1988)A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test (1999)Unit root tests for panel data (2001)Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework (2014)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)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)Analysis of Inventory Systems. (1964)Time Series Analysis: Forecasting and Control. (1978)Practical Bayesian Optimization of Machine Learning Algorithms (2012)A data-driven newsvendor problem: From data to decision (2019)A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization (2021)A survey of contextual optimization methods for decision-making under uncertainty (2024)Dynamic Version of the Economic Lot Size Model (1958)Dynamic Version of the Economic Lot Size Model (2004)Forecasting: theory and practice (2022)From natural language to simulations: applying AI to automate simulation modelling of logistics systems (2023)Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability (2023)Generalized autoregressive conditional heteroskedasticity (1986)Co-Integration and Error Correction: Representation, Estimation, and Testing (1987)Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results (2003)Analysis of Financial Time Series (2006)Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure (2016)Studies in Mathematical Theory of Inventory and Production (1959)The Distribution Free Newsboy Problem: Review and Extensions (1993)Lectures on Stochastic Programming (2009)Inventory management and production planning and scheduling (1999)Pricing and the Newsvendor Problem: A Review with Extensions (1999)Retail forecasting: Research and practice (2019)Typology and literature review on multiple supplier inventory control models (2020)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)
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References

  • 1992Testing the null hypothesis of stationarity against the alternative of a unit root12,637 cited
  • 2002Introduction to Time Series and Forecasting3,934 cited
  • 2021Smart “Predict, then Optimize”880 cited
  • 2019From Predictive to Prescriptive Analytics868 cited
  • 2018The Big Data Newsvendor: Practical Insights from Machine Learning531 cited
  • 2007Provably Near-Optimal Sampling-Based Policies for Stochastic Inventory Control Models261 cited
  • 2015The Data-Driven Newsvendor Problem: New Bounds and Insights260 cited
  • 2020A Practical End-to-End Inventory Management Model with Deep Learning178 cited
  • 2019Applying deep learning to the newsvendor problem163 cited
  • 2011Safety stock planning under causal demand forecasting152 cited
  • 2007Solving operational statistics via a Bayesian analysis105 cited
  • 2023How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time70 cited
  • 2019Quantile forecasting and data-driven inventory management under nonstationary demand59 cited
  • 2015Robust newsvendor problem with autoregressive demand48 cited
  • 2022An integrated data-driven method using deep learning for a newsvendor problem with unobservable features

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47 cited
  • 2021Distributionally Robust Conditional Quantile Prediction with Fixed Design45 cited
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