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

Content Provider	Springer Nature Link
Author	Claro, João Sousa, Jorge Pinho
Copyright Year	2008
Abstract	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.
Starting Page	427
Ending Page	450
Page Count	24
File Format	PDF
ISSN	09266003
Journal	Computational Optimization and Applications
Volume Number	46
Issue Number	3
e-ISSN	15732894
Language	English
Publisher	Springer US
Publisher Date	2008-09-03
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Stochastic knapsack problem Stochastic combinatorial optimisation Mean-risk objectives Multiobjective combinatorial optimisation Multiobjective metaheuristics Convex and Discrete Geometry Statistics Operations Research/Decision Theory Operations Research, Mathematical Programming Optimization
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Control and Optimization Computational Mathematics

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