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


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


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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