A mathematical view of interior-point methods in convex optimization
A mathematical view of interior-point methods in convex optimization
A Joint Location-Inventory Model
Transportation Science
Pricing and the News Vendor Problem: a Review with Extensions
Operations Research
Semivariance criteria for quantifying the choice among uncertain outcomes
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
A Stackelberg solution for fuzzy random competitive location problems with demand site uncertainty
Intelligent Decision Technologies
Analytic network process in risk assessment and decision analysis
Computers and Operations Research
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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.