Technical Note—Nonparametric Data-Driven Algorithms for Multiproduct Inventory Systems with Censored Demand
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Publication:5740215
DOI10.1287/opre.2015.1474zbMath1342.90014OpenAlexW2314247578MaRDI QIDQ5740215
Izak Duenyas, Cong Shi, Weidong Chen
Publication date: 25 July 2016
Published in: Operations Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1287/opre.2015.1474
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Cites Work
- Unnamed Item
- Solving operational statistics via a Bayesian analysis
- A practical inventory control policy using operational statistics
- The Sample Average Approximation Method for Stochastic Discrete Optimization
- The Data-Driven Newsvendor Problem: New Bounds and Insights
- Adaptive Data-Driven Inventory Control with Censored Demand Based on Kaplan-Meier Estimator
- An Adaptive, Distribution-Free Algorithm for the Newsvendor Problem with Censored Demands, with Applications to Inventory and Distribution
- Stalking Information: Bayesian Inventory Management with Unobserved Lost Sales
- Online Learning and Online Convex Optimization
- A Nonparametric Asymptotic Analysis of Inventory Planning with Censored Demand
- An Adaptive Algorithm for Finding the Optimal Base-Stock Policy in Lost Sales Inventory Systems with Censored Demand
- Using Stochastic Approximation Methods to Compute Optimal Base-Stock Levels in Inventory Control Problems
- Robust Stochastic Approximation Approach to Stochastic Programming
- An explicit upper bound for the mean busy period in a GI/G/1 queue
- Logarithmic Regret Algorithms for Online Convex Optimization
- Provably Near-Optimal Sampling-Based Policies for Stochastic Inventory Control Models
- Optimality of Myopic Inventory Policies for Several Substitute Products
- Learning Algorithms for Separable Approximations of Discrete Stochastic Optimization Problems
- Stochastic multiproduct inventory models with limited storage
