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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2010

Autores

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

Revista

Título: Computational Optimization and Applications

Vol. 46 Nº 3

Páginas: 427-450

ISSN: 0926-6003

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 5 Citações

ISI Web of Science

Scopus - 5 Citações

INSPEC

COMPENDEX

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

CORDIS: Ciências Físicas > Matemática > Matemática computacional ; Ciências Físicas > Matemática > Matemática aplicada > Investigação operacional ; Ciências Tecnológicas > Engenharia > Engenharia de projecto

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica