Solving stochastic programming models in supply chain design using sampling heuristics

Yin Mo; Terry P. Harrison; Russell R. Barton

doi:10.1093/imaman/dpq003

Solving stochastic programming models in supply chain design using sampling heuristics

Yin Mo, Terry P. Harrison, Russell R. Barton

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

We consider a supply chain design problem to provide new ways to buffer the effect of demand uncertainty when designing supply chains. The problem is formulated as a two-stage stochastic programming with fixed recourse. A special sampling procedure based on uniform experiment designs provides superior quality approximate solutions for a simple supply chain structure when demands are correlated.

Original language	English (US)
Pages (from-to)	65-77
Number of pages	13
Journal	IMA Journal of Management Mathematics
Volume	22
Issue number	1
DOIs	https://doi.org/10.1093/imaman/dpq003
State	Published - Jan 2011

All Science Journal Classification (ASJC) codes

Management Information Systems
Modeling and Simulation
Economics, Econometrics and Finance(all)
Strategy and Management
Management Science and Operations Research
Applied Mathematics

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10.1093/imaman/dpq003

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abstract = "We consider a supply chain design problem to provide new ways to buffer the effect of demand uncertainty when designing supply chains. The problem is formulated as a two-stage stochastic programming with fixed recourse. A special sampling procedure based on uniform experiment designs provides superior quality approximate solutions for a simple supply chain structure when demands are correlated.",

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Solving stochastic programming models in supply chain design using sampling heuristics

Abstract

All Science Journal Classification (ASJC) codes

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