Two-stage stochastic programming involving CVaR with an application to disaster management

Noyan, Nilay (2010) Two-stage stochastic programming involving CVaR with an application to disaster management. (Submitted)

WarningThere is a more recent version of this item available.

PDF (This is a RoMEO green publisher -- author can archive pre-print (ie pre-refereeing)) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


Traditional two-stage stochastic programming is risk-neutral; that is, it considers the expectation as the preference criterion while comparing the random variables (e.g., total cost) to identify the best decisions. However, in the presence of variability risk measures should be incorporated into decision making problems in order to model its effects. In this study, we consider a risk-averse two-stage stochastic programming model, where we specify the conditional-value-at-risk (CVaR) as the risk measure. We construct two decomposition algorithms based on the generic Benders-decomposition approach to solve such problems. Both single-cut and multicut versions of the proposed decomposition algorithms are presented. We apply the proposed framework to disaster management, which is one of the research fields that can significantly benefit from risk-averse two-stage stochastic programming models. In particular, we consider the problem of determining the response facility locations and the inventory levels of the relief supplies at each facility in the presence of uncertainty in demand and the damage level of the disaster network. We present numerical results to discuss how incorporating a risk measure affects the optimal solutions and to demonstrate the computational efficiency of the proposed methods.

Item Type:Article
Subjects:Q Science > Q Science (General)
ID Code:14970
Deposited By:Nilay Noyan
Deposited On:02 Nov 2010 16:26
Last Modified:20 Oct 2011 14:36

Available Versions of this Item

Repository Staff Only: item control page