CVaR-based factorial stochastic optimization of water resources systems with correlated uncertainties

Y. Y. Wang, G. H. Huang, Shuo Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

Abstract

In this paper, a conditional value-at-risk based factorial stochastic programming approach is proposed to address random uncertainties and their interactions in a systematic manner. Random variables can be addressed through a risk-averse method within the two-stage stochastic programming framework. Interactions between random variables are examined through conducting a multi-level factorial analysis. The proposed approach is applied to a case study of water resources management to demonstrate its validity and applicability. A number of decision alternatives are obtained under different risk coefficients, which are useful for decision-makers to make sound water management plan and to perform an in-depth analysis of trade-offs between economic objectives and associated risks. Results obtained from the factorial experiment uncover the multi-level interactions between uncertain parameters and their contributions to the variability of net benefits. The performance of the proposed approach is compared with a factorial two-stage stochastic programming method.
Original languageEnglish
Pages (from-to)1543-1553
Number of pages11
JournalStochastic Environmental Research and Risk Assessment
Volume31
Issue number6
DOIs
Publication statusPublished - 1 Aug 2017
Externally publishedYes

Keywords

  • CVaR
  • Factorial design
  • Interactive uncertainties
  • Stochastic programming
  • Water resources allocation
  • Water system

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Water Science and Technology
  • Safety, Risk, Reliability and Quality
  • Environmental Science(all)

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