Augmented Markov Chain Monte Carlo Simulation for Two-Stage Stochastic Programs with Recourse

Tahir Ekin (), Nicholas G. Polson () and Refik Soyer ()
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Tahir Ekin: McCoy College of Business, Texas State University, San Marcos, Texas 78666
Nicholas G. Polson: Booth School of Business, University of Chicago, Chicago, Illinois 60637
Refik Soyer: School of Business, George Washington University, Washington, DC 20052

Decision Analysis, 2014, vol. 11, issue 4, 250-264

Abstract: In this paper, we develop a simulation-based approach for two-stage stochastic programs with recourse. We construct an augmented probability model with stochastic shocks and decision variables. Simulating from the augmented probability model solves for the expected recourse function and the optimal first-stage decision. Markov chain Monte Carlo methods, together with ergodic averaging, provide a framework to compute the optimal solution. We illustrate our methodology via the two-stage newsvendor problem with unimodal and bimodal continuous uncertainty. Finally, we present performance comparisons of our algorithm and the sample average approximation method.

Keywords: decision analysis; dynamic decision making; math programming; optimization; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ordeca:v:11:y:2014:i:4:p:250-264

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