Tuesday, 15:15 - 15:40 h, Room: MA 144


Kathrin Klamroth
Modeling uncertainties in location-allocation problems: A stochastic programming approach

Coauthor: Markus Kaiser


Knowledge about the future development of the planning area and the environmental conditions is highly important for location decisions.
We consider continuous location-allocation problems with Weber objectives where uncertainty not only occurs in the demand of the existing facilities (that is, in the location objective), but also in the constraints of the problem such as the size and the shape of the feasible region. The trade-off between the cost of a solution on one hand and its robustness with respect to the uncertain data on the other hand is analyzed, which naturally motivates a multiple objective formulation of the problem. A two-stage stochastic programming model is obtained as a scalarization of the multiple objective model, and the relations to single-objective location-allocation problems are discussed. We use geometric arguments to derive discretization results for the case that distances are measured by block norms or polyhedral gauges. An efficient location-allocation heuristic for problems with uncertain feasible sets is suggested and tested on problem data with up to 2000 demand nodes, 10 different scenarios and 10 new facilities.


Talk 1 of the contributed session Tue.3.MA 144
"Nonlinear stochastic optimization" [...]
Cluster 22
"Stochastic optimization" [...]


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