Using the WOWA operator in robust discrete optimization problems

TL;DR

This paper explores the use of the WOWA operator in solving discrete optimization problems with uncertain costs, incorporating scenario probabilities and decision-maker attitudes, and provides algorithms and computational results.

Contribution

It introduces a framework applying the WOWA operator to robust discrete optimization, including complexity analysis and solution algorithms.

Findings

Complexity characterization of the problem class

Development of exact and approximation algorithms

Computational tests demonstrating effectiveness

Abstract

In this paper a class of discrete optimization problems with uncertain costs is discussed. The uncertainty is modeled by introducing a scenario set containing a finite number of cost scenarios. A probability distribution in the scenario set is available. In order to choose a solution the weighted OWA criterion (WOWA) is applied. This criterion allows decision makers to take into account both probabilities for scenarios and the degree of pessimism/ optimism. In this paper the complexity of the considered class of discrete optimization problems is described and some exact and approximation algorithms for solving it are proposed. An application to a selection problem, together with results of computational tests are shown.