Monday, 10:30 - 10:55 h, Room: H 0106


Sergio Garcia Quiles
On the p-median problem with uncertainty in the cost matrix

Coauthor: Laureano F. Escudero


It has been shown that the deterministic p-median problem can be solved with just a column generation approach that is embedded in a branch-and-bound framework. Large-scale instances have been solved efficiently in a small computing time. However, quite often, the cost matrix coefficients are random variables. The aim of this paper is twofold. First, a model is presented to minimize the expected cost over a set of scenarios of the outlook of the random variables while satisfying the first order stochastic dominance constraints (sdc) for a set of profiles in order to reduce the risk of the cost impact of the solution in non-wanted scenarios. And second, a scheme to obtain the solution of the stochastic p-median problem is developed by considering the splitting variable representation of the static Deterministic Equivalent Model (DEM) of the stochastic one. This scheme dualizes the non-anticipativity constraints and treats with a special procedure the sdc for each profile (since those constraints have variables from different scenarios). A computational experience is reported.


Talk 1 of the contributed session Mon.1.H 0106
"Facility location and p-median problems" [...]
Cluster 13
"Logistics, traffic, and transportation" [...]


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