.png){kind=icon}
Return
Approximating combinatorial optimization problems with the ordered weighted averaging criterion
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2020.04.018.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Robustness and sensitivity analysis
Ordered weighted averaging
Combinatorial optimization
Approximation algorithms
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W
Organization
Cited Papers
Effects of Group Counseling Transmitted Through Videoconferencing on Changes in Eating Behaviors
.png){kind=icon}
PREAI
ShareSave
ShareSaveShareSaveShareSaveShareSaveAnalysis of prognostic factors in male breast cancer: a report of 72 cases from a single institution
OAAI
OAAIShareSaveShareSaveShareSave
View more