Risk criteria in a stochastic knapsack problem
Operations Research
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Scenarios and policy aggregation in optimization under uncertainty
Mathematics of Operations Research
Parallel biased search for combinatorial optimization: genetic algorithms and TABU
Microprocessors & Microsystems
An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem
Discrete Applied Mathematics - Special volume: viewpoints on optimization
On a stochastic knapsack problem and generalizations
Advances in computational and stochastic optimization, logic programming, and heuristic search
An interactive heuristic method for multi-objective combinatorial optimization
Computers and Operations Research - Special issue on artificial intelligence and decision support with multiple criteria
Heuristics for cardinality constrained portfolio optimisation
Computers and Operations Research
The Sample Average Approximation Method for Stochastic Discrete Optimization
SIAM Journal on Optimization
Dual Stochastic Dominance and Related Mean-Risk Models
SIAM Journal on Optimization
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Computers and Operations Research
Polyhedral Risk Measures in Stochastic Programming
SIAM Journal on Optimization
Convexity and decomposition of mean-risk stochastic programs
Mathematical Programming: Series A and B
Introducing robustness in multi-objective optimization
Evolutionary Computation
Optimization of Convex Risk Functions
Mathematics of Operations Research
Trade-off between performance and robustness: an evolutionary multiobjective approach
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
On deviation measures in stochastic integer programming
Operations Research Letters
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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.