  


Meanrisk objectives in stochastic programming
Shabbir Ahmed (sahmedisye.gatech.edu) Abstract: Traditional stochastic programming is risk neutral in the sense that it is concerned with the optimization of an expectation criteria. A common approach to addressing risk in decision making problems is to consider a weighted meanrisk criterion, where some dispersion statistic is used as a measure of risk. We investigate the computational suitability of various meanrisk objective functions in addressing risk in stochastic programming models. We prove that the classical meanvariance criteria leads to computational intractability even in the simplest stochastic programs. On the other hand, a number of alternative meanrisk functions are shown to be computationally tractable using slight variants of existing stochastic programming decomposition algorithms. We propose a parametric cutting plane algorithm to generate the entire meanrisk efficient frontier for a particular meanrisk objective. Keywords: Stochastic programming, meanrisk objectives, computational complexity, cutting plane algorithms. Category 1: Stochastic Programming Category 2: Convex and Nonsmooth Optimization (Nonsmooth Optimization ) Citation: Technical Report. School of Industrial & Systems Engineering, Georgia Institute of Technology. Download: [PDF] Entry Submitted: 04/12/2004 Modify/Update this entry  
Visitors  Authors  More about us  Links  
Subscribe, Unsubscribe Digest Archive Search, Browse the Repository

Submit Update Policies 
Coordinator's Board Classification Scheme Credits Give us feedback 
Optimization Journals, Sites, Societies  