Weak aggregating algorithm for the distribution-free perishable inventory problem

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.

• Type: Journal
• Year: 2010
• Where: ORL
• Authors: Tatsiana Levina, Yuri Levin, Jeff McGill, Mikhail Nediak, Vladimir Vovk
• Keywords: Asymptotic Average Performance, Operations Research, ORL 2010, Perishable Inventory Problem, Weak Aggregating Algorithm