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Approximating combinatorial optimization problems with the ordered weighted averaging criterion
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DOI：10.1016/j.ejor.2020.04.018.png)
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This paper deals with combinatorial optimization problems with K cost scenarios, inducing K linear objectives. The popular Ordered Weighted Averaging (OWA) criterion is used to aggregate the objectives and compute a solution. It is well-known that minimizing OWA for most basic combinatorial problems is weakly NP-hard even if the number of scenarios K equals two, and strongly NP-hard when K is a part of the input. In this paper, the problem with nonincreasing weights in the OWA criterion and a large K is first considered. A method of reducing the number of scenarios, by appropriately aggregating the costs before solving the problem, is proposed. It is shown that an optimal solution to the reduced problem has a guaranteed worst-case approximation ratio. Some new approximation results for the Hurwicz criterion, which is a special case of OWA, are also presented. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Robustness and sensitivity analysis
Ordered weighted averaging
Combinatorial optimization
Approximation algorithms
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IF:
6
论文数:
2.2W
被引数:
6.4W
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