Decision rule approximations for the risk averse reservoir management problem

Charles Gauvin, Erick Delage and Michel Gendreau

European Journal of Operational Research, 2017, vol. 261, issue 1, 317-336

Abstract: This paper presents a new formulation for the risk averse stochastic reservoir management problem. Using recent advances in robust optimization and stochastic programming, we propose a multi-stage model based on minimization of a risk measure associated with floods and droughts for a hydro-electrical complex. We present our model and then identify approximate solutions using standard affine decision rules commonly found in the literature as well as lifted decision rules. Finally, we conduct thorough numerical experiments based on a real river system in Western Québec and conclude on the relative performance of families of decision rules.

Keywords: Stochastic programming; Robust optimization; Risk analysis; OR in energy (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221717300796
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:261:y:2017:i:1:p:317-336

DOI: 10.1016/j.ejor.2017.01.044

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().