Risk Management in Uncapacitated Facility Location Models with Random Demands

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Joyendu Bhadury, Professor, Information Systems and Supply Chain Management (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: In this paper we consider a location-optimization problem where the classical uncapacitated facility location model is recast in a stochastic environment with several risk factors that make demand at each customer site probabilistic and correlated with demands at the other customer sites. Our primary contribution is to introduce a new solution methodology that adopts the mean–variance approach, borrowed from the finance literature, to optimize the “Value-at-Risk” (VaR) measure in a location problem. Specifically, the objective of locating the facilities is to maximize the lower limit of future earnings based on a stated confidence level. We derive a nonlinear integer program whose solution gives the optimal locations for the p facilities under the new objective. We design a branch-and-bound algorithm that utilizes a second-order cone program (SOCP) solver as a subroutine. We also provide computational results that show excellent solution times on small to medium sized problems.

Additional Information

Computers & Operations Research, 36(4), 1002-1011
Language: English
Date: 2009
Facility location, Risk management, Second-order cone programming, Value-at-Risk

Email this document to