Technical Note—Nonparametric Data-Driven Algorithms for Multiproduct Inventory Systems with Censored Demand

Cong Shi (), Weidong Chen () and Izak Duenyas ()
Additional contact information
Cong Shi: Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Weidong Chen: Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Izak Duenyas: Technology and Operations, Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109

Operations Research, 2016, vol. 64, issue 2, 362-370

Abstract: We propose a nonparametric data-driven algorithm called DDM for the management of stochastic periodic-review multiproduct inventory systems with a warehouse-capacity constraint. The demand distribution is not known a priori and the firm only has access to past sales data (often referred to as censored demand data). We measure performance of DDM through regret, the difference between the total expected cost of DDM and that of an oracle with access to the true demand distribution acting optimally. We characterize the rate of convergence guarantee of DDM. More specifically, we show that the average expected T -period cost incurred under DDM converges to the optimal cost at the rate of O ( T −1/2 ). Our asymptotic analysis significantly generalizes approaches used in Huh and Rusmevichientong (2009) for the uncapacitated single-product inventory systems. We also discuss several extensions and conduct numerical experiments to demonstrate the effectiveness of our proposed algorithm.

Keywords: inventory; multiproduct; censored demand; nonparametric algorithms; asymptotic analysis (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.2015.1474 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:64:y:2016:i:2:p:362-370

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().