Monday, 16:15 - 16:40 h, Room: H 3503


Mohammad Mehdi Nasrabadi
A fuzzy programming approach to robust optimization


A crucial feature of linear programming occurring in real-world applications is that all or some of parameters are uncertain. Robust optimization has attracted a great deal of attention to address this situation. We consider robust linear programs, where the parameters in the constraint matrix are uncertain but known to lie in a given deterministic uncertainty set. We present a fuzzy programming approach to soften the hard constraints of the robust optimization. In particular, given a feasible solution, we introduce a membership function for each constraint to indicate how much the constraint is violated in the worst-case. We characterize the three basic ingredients in fuzzy decision making, that are, fuzzy goal, fuzzy constraint, and fuzzy decision. We then present an algorithm for solving the robust linear program with softness constraints based on the well-known approach of Bellman and Zadeh (1970) in fuzzy programming. We show that the problem is efficiently solvable when the uncertain parameters are the ones considered by Bertsimas and Sim (2003).


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


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