Title:
Simple Newsvendor Heuristics for Two-Echelon Distribution Networks

dc.contributor.author Ferguson, Mark E.
dc.contributor.author Lystad, Erik D.
dc.date.accessioned 2006-03-08T03:06:33Z
dc.date.available 2006-03-08T03:06:33Z
dc.date.issued 2006-03-08
dc.description.abstract We consider the problem of determining stocking levels in a multi-echelon distribution network consisting of a warehouse and n non-identical retail locations. Lead-times are deterministic, there are no fixed ordering costs, and unmet demand is backlogged. Both Clark and Scarf (1960) and Federgruen and Zipkin (1984b) propose heuristic solutions for such a problem based on a stochastic dynamic programming formulation. The disadvantage of their formulations lies in the very large state space needed for its solution. For a serial supply chains, Shang and Song (2003) provide single period newsvendor problems that bound the optimal stocking levels determined by the Clark and Scarf (1960) serial supply chain model. Newsvendor bounds have a number of valuable qualities; they are considerably less computationally intensive, allow for ready parametric analysis, and facilitate the development of intuition. In this paper, we extend the newsvendor bounds technique to distribution systems, thus providing a simple and surprisingly accurate heuristic. Through a simulation study, we show that our heuristic significantly outperforms other common heuristics over a wide range of parameter values. The closed form solutions provided by the newsvendor bounds also allows us to gain insights into the system behavior of a distribution network that was not previously possible through alternative solution techniques. en
dc.format.extent 612113 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/8086
dc.language.iso en_US en
dc.subject Distribution systems en
dc.subject Multi-echelon en
dc.title Simple Newsvendor Heuristics for Two-Echelon Distribution Networks en
dc.type Text
dc.type.genre Working Paper
dspace.entity.type Publication
local.contributor.corporatename Scheller College of Business
relation.isOrgUnitOfPublication a2f83831-ae41-4d65-82ff-c8bf95db4ffb
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