A multiobjective metaheuristic for a mean-risk static stochastic knapsack problem

• Type: Article in International Scientific Journal
• Date: 2010

Authors

João Claro (Author) FEUP; Jorge Pinho de Sousa (Author) FEUP

Journal

Title: Computational Optimization and Applications

Vol. 46 No. 3

Pages: 427-450

ISSN: 0926-6003

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 5 Citations

ISI Web of Science

Scopus - 5 Citations

INSPEC

COMPENDEX

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Physical sciences > Mathematics > Computational mathematics ; Physical sciences > Mathematics > Applied mathematics > Operations research ; Technological sciences > Engineering > Project engineering

Other information

Authenticus ID: P-003-54H

DOI: 10.1007/s10589-008-9197-2

Abstract (EN): In this paper we address two major challenges presented by stochastic discrete optimisation problems: the multiobjective nature of the problems, once risk aversion is incorporated, and the frequent difficulties in computing exactly, or even approximately, the objective function. The latter has often been handled with methods involving sample average approximation, where a random sample is generated so that population parameters may be estimated from sample statistics—usually the expected value is estimated from the sample average. We propose the use of multiobjective metaheuristics to deal with these difficulties, and apply a multiobjective local search metaheuristic to both exact and sample approximation versions of a mean-risk static stochastic knapsack problem. Variance and conditional value-at-risk are considered as risk measures. Results of a computational study are presented, that indicate the approach is capable of producing high-quality approximations to the efficient sets, with a modest computational effort.

Language: English

Type (Professor's evaluation): Scientific