Robust strategies for facility location under uncertainty

► Tractable robust approaches for the stochastic facility location problem are studied. ► Advanced uncertainty sets are used to model the uncertainty in customer demand. ► Effective robust approximations are used to handle probabilistic constraints. ► Risk references are incorporated in the suggeste...

Full description

Saved in:
Bibliographic Details
Published in:European journal of operational research 2013-02, Vol.225 (1), p.21-35
Main Authors: Gülpınar, Nalan, Pachamanova, Dessislava, Çanakoğlu, Ethem
Format: Article
Language:English
Subjects:
Ambiguous demand distributions
Approximation
Customers
Decision analysis
Demand
Expected values
Location analysis
Marketing
Mathematical models
Mathematical problems
Optimization
Position (location)
Robust approximations to chance constraints
Robust facility location decisions
Robust inventory management
Strategy
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:► Tractable robust approaches for the stochastic facility location problem are studied. ► Advanced uncertainty sets are used to model the uncertainty in customer demand. ► Effective robust approximations are used to handle probabilistic constraints. ► Risk references are incorporated in the suggested robust framework. ► Robust strategies lead to better worst-case performance than stochastic ones. This paper considers a stochastic facility location problem in which multiple capacitated facilities serve customers with a single product, and a stockout probabilistic requirement is stated as a chance constraint. Customer demand is assumed to be uncertain and to follow either a normal or an ambiguous distribution. We study robust approximations to the problem in order to incorporate information about the random demand distribution in the best possible, computationally tractable way. We also discuss how a decision maker’s risk preferences can be incorporated in the problem through robust optimization. Finally, we present numerical experiments that illustrate the performance of the different robust formulations. Robust optimization strategies for facility location appear to have better worst-case performance than nonrobust strategies. They also outperform nonrobust strategies in terms of realized average total cost when the actual demand distributions have higher expected values than the expected values used as input to the optimization models.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2012.08.004