A two-stage stochastic programming model for electric energy producers
Computers and Operations Research
A two-stage stochastic programming model for transportation network protection
Computers and Operations Research
Operations Research
A multiobjective metaheuristic for a mean-risk multistage capacity investment problem
Journal of Heuristics
A multiobjective metaheuristic for a mean-risk static stochastic knapsack problem
Computational Optimization and Applications
Risk-Averse Two-Stage Stochastic Linear Programming: Modeling and Decomposition
Operations Research
A decision support system for strategic asset allocation
Decision Support Systems
Risk-averse two-stage stochastic programming with an application to disaster management
Computers and Operations Research
Capital rationing problems under uncertainty and risk
Computational Optimization and Applications
Polymatroids and mean-risk minimization in discrete optimization
Operations Research Letters
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Traditional stochastic programming is risk neutral in the sense that it is concerned with the optimization of an expectation criterion. A common approach to addressing risk in decision making problems is to consider a weighted mean-risk objective, where some dispersion statistic is used as a measure of risk. We investigate the computational suitability of various mean-risk objective functions in addressing risk in stochastic programming models. We prove that the classical mean-variance criterion leads to computational intractability even in the simplest stochastic programs. On the other hand, a number of alternative mean-risk functions are shown to be computationally tractable using slight variants of existing stochastic programming decomposition algorithms. We propose decomposition-based parametric cutting plane algorithms to generate mean-risk efficient frontiers for two particular classes of mean-risk objectives.