Algorithmic Developments for Difficult Robust Discrete Optimization Problems

  • Panos Kouvelis
  • Gang Yu
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 14)


In Chapter 4, several polynomially solvable cases of robust discrete optimization problems are discussed in detail. However, by results presented in Chapter 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 with equivalent single scenario problems that can be efficiently solved with a polynomial or pseudo-polynomial procedure. The solution procedures are based on branch-and-bound with both upper and lower bounds generated by surrogate relaxation. To be exact, the upper bound (for a maximization problem) is obtained from surrogate relaxation and the lower bound is obtained as a by-product via a heuristic based on the surrogate relaxation result. In the case when input data satisfies bounded percentage deviation condition (to be defined in Section 5.2), the heuristic is shown to provide a constant approximation. Computational results in Section 5.3 demonstrate the effectiveness of the bounds and the solution procedure.


Algorithmic Development Knapsack Problem Short Path Problem Resource Allocation Problem Greedy Heuristic 
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Copyright information

© Springer Science+Business Media Dordrecht 1997

Authors and Affiliations

  • Panos Kouvelis
    • 1
  • Gang Yu
    • 2
  1. 1.Olin School of BusinessWashington University at St. LouisSt. LouisUSA
  2. 2.Center for Cybernetic StudiesThe University of TexasAustinUSA

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