Scenario generation in stochastic programming using principal component analysis based on moment-matching approach

Isha Chopra () and Dharmaraja Selvamuthu ()
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Isha Chopra: Indian Institute of Technology Delhi
Dharmaraja Selvamuthu: Indian Institute of Technology Delhi

OPSEARCH, 2020, vol. 57, issue 1, No 10, 190-201

Abstract: Abstract In optimization models based on stochastic programming, we often face the problem of representing expectations in proper form known as scenario generation. With advances in computational power, a number of methods starting from simple Monte-Carlo to dedicated approaches such as method of moment-matching and scenario reduction are being used for multistage scenario generation. Recently, various variations of moment-matching approach have been proposed with the aim to reduce computational time for better outputs. In this paper, we describe a methodology to speed up moment-matching based multistage scenario generation by using principal component analysis. Our proposal is to pre-process the data using dimensionality reduction approaches instead of using returns as variables for moment-matching problem directly. We also propose a hybrid multistage extension of heuristic based moment-matching algorithm and compare it with other variants of moment-matching algorithm. Computational results using non-normal and correlated returns show that the proposed approach provides better approximation of returns distribution in lesser time.

Keywords: Scenario generation; Moment-matching; Principal component analysis; Stochastic programming (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s12597-019-00418-8

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