Weak aggregating algorithm for the distribution-free perishable inventory problem

Authors:
Tatsiana Levina;Yuri Levin;Jeff Mcgill;Mikhail Nediak;Vladimir Vovk
Affiliations:
School of Business, Queen's University, 143 Union St., Kingston, ON, K7L 3N6, Canada;School of Business, Queen's University, 143 Union St., Kingston, ON, K7L 3N6, Canada;School of Business, Queen's University, 143 Union St., Kingston, ON, K7L 3N6, Canada;School of Business, Queen's University, 143 Union St., Kingston, ON, K7L 3N6, Canada;Department of Computer Science, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK
Venue:
Operations Research Letters
Year:
2010

Citing 7
Cited 0

Prediction, Learning, and Games

Prediction, Learning, and Games
The weak aggregating algorithm and weak mixability

Journal of Computer and System Sciences
Provably Near-Optimal Sampling-Based Policies for Stochastic Inventory Control Models

Mathematics of Operations Research
Analysis of Perishable-Inventory Systems with Censored Demand Data

Operations Research
A Nonparametric Asymptotic Analysis of Inventory Planning with Censored Demand

Mathematics of Operations Research
Solving operational statistics via a Bayesian analysis

Operations Research Letters
A practical inventory control policy using operational statistics

Operations Research Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

We formulate the multiperiod, distribution-free perishable inventory problem as a problem of prediction with expert advice and apply an online learning method (the Weak Aggregating Algorithm) to solve it. We show that the asymptotic average performance of this method is as good as that of any time-dependent stocking rule in a given parametric class.