Titlebook: Robust Discrete Optimization and Its Applications; Panos Kouvelis,Gang Yu Book 1997 Springer Science+Business Media Dordrecht 1997 Mathema

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Easily Solvable Cases of Robust Discrete Optimization Problems,s an optimistic tone by describing polynomially solvable problems. The main source of difficulty of robust optimization problems comes from its min-max (or max-min) nature and its added dimensionality — the scenario sets. In many cases where both the decision variables and the scenario sets are cont

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Algorithmic Developments for Difficult Robust Discrete Optimization Problems,hapter 3 we also know that most robust discrete optimization problems belong to the NP-hard class. In this chapter, we present our approach for solving these difficult robust discrete optimization problems. We are in this chapter restricting our attention to robust discrete optimization problems wit

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Robust 1-Median Location Problems: Dynamic Aspects and Uncertainty, problem referred to as the dynamically robust 1-median location on a tree. The robust 1-median on a tree problem, as introduced in Chapter 2, addresses the location of a single facility on a tree network in the presence of significant uncertainty in the node weights (node demands) and edge lengths

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Robust Scheduling Problems,over that a schedule which is optimal with respect to a deterministic or stochastic scheduling model yields quite poor performance when evaluated relative to the actual processing times. In these environments, the notion of schedule robustness, i.e., determining the schedule with the best worst-case

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Easily Solvable Cases of Robust Discrete Optimization Problems,tability requirement is a luxury in discrete optimization. Even the primal and its relaxation dual will in most cases inevitably lead to a gap between the corresponding objective values. We believe that the number of polynomially solvable discrete robust optimization problems is very limited.

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