Optimal Policies for Multiechelon Inventory Problems with Markov-Modulated Demand

Fangruo Chen () and Jing-Sheng Song ()
Additional contact information
Fangruo Chen: Graduate School of Business, Columbia University, New York, New York 10027
Jing-Sheng Song: Graduate School of Management, University of California, Irvine, California 92697

Operations Research, 2001, vol. 49, issue 2, 226-234

Abstract: This paper considers a multistage serial inventory system with Markov-modulated demand. Random demand arises at Stage 1, Stage 1 orders from Stage 2, etc., and Stage N orders from an outside supplier with unlimited stock. The demand distribution in each period is determined by the current state of an exogenous Markov chain. Excess demand is backlogged. Linear holding costs are incurred at every stage, and linear backorder costs are incurred at Stage 1. The ordering costs are also linear. The objective is to minimize the long-run average costs in the system. The paper shows that the optimal policy is an echelon base-stock policy with state-dependent order-up-to levels. An efficient algorithm is also provided for determining the optimal base-stock levels. The results can be extended to serial systems in which there is a fixed ordering cost at stage N and to assembly systems with linear ordering costs.

Keywords: Inventory/Production: multi-echelon; Markov-modulated infinite horizon; optimal policy (search for similar items in EconPapers)
Date: 2001
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (62)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.49.2.226.13528 (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:49:y:2001:i:2:p:226-234

Access Statistics for this article

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