Monday, 15:45 - 16:10 h, Room: H 3503

 

Michael Römer
Linear optimization with variable parameters: Robust and generalized linear programming and their relations

 

Abstract:
In linear programming, it is usually assumed that the problem data is certain and fixed. In many real world situations, however, the parameters are subject to variation. In a pessimistic scenario, the variation is not controllable by the decision maker: This is the case for parameters affected by measurement errors or uncertainty. One way to deal with such a situation is to employ robust linear programming to obtain a solution that is feasible for all elements of a given parameter uncertainty set.
In an optimistic scenario, the variation can be controlled: Some coefficients may represent adjustable technical parameters or can be influenced by higher-level decisions. A possible approach to model this setting is generalized linear programming. In this approach, going back to early work of Dantzig and Wolfe, a solution is sought which is feasible for at least one parameter combination from a given variation set.
In this work, we provide a unified view of robust and generalized linear programs and their compact reformulations. We discuss the dual relation of both approaches and show how this duality may contribute to a deeper understanding and a mutual stimulation of both fields.

 

Talk 2 of the contributed session Mon.3.H 3503
"Extensions of robust optimization approaches" [...]
Cluster 20
"Robust optimization" [...]

 

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