Scalable modeling and solution of stochastic multiobjective optimization problems

•Present a computing framework for stochastic multiobjective optimization.•Use a nested CVaR metric to trade-off multiple random objectives.•Show that nested CVaR can be formulated as a standard NLP.•Present a combined heat and power study to demonstrate developments. We present a scalable computing...

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Bibliographic Details
Published in:Computers & chemical engineering Vol. 99; no. C; pp. 185 - 197
Main Authors: Cao, Yankai, Fuentes-Cortes, Luis Fabian, Chen, Siyu, Zavala, Victor M.
Format: Journal Article
Language:English
Published: United Kingdom Elsevier Ltd 06.04.2017
Elsevier
Subjects:
CVaR
Large scale
Multiobjective
Optimization
Stochastic
Multiobjective
Large scale
Stochastic
CVaR
Optimization
ISSN:0098-1354, 1873-4375
Online Access:Get full text
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Summary:•Present a computing framework for stochastic multiobjective optimization.•Use a nested CVaR metric to trade-off multiple random objectives.•Show that nested CVaR can be formulated as a standard NLP.•Present a combined heat and power study to demonstrate developments. We present a scalable computing framework for the solution stochastic multiobjective optimization problems. The proposed framework uses a nested conditional value-at-risk (nCVaR) metric to find compromise solutions among conflicting random objectives. We prove that the associated nCVaR minimization problem can be cast as a standard stochastic programming problem with expected value (linking) constraints. We also show that these problems can be implemented in a modular and compact manner using PLASMO (a Julia-based structured modeling framework) and can be solved efficiently using PIPS-NLP (a parallel nonlinear solver). We apply the framework to a CHP design study in which we seek to find compromise solutions that trade-off cost, water, and emissions in the face of uncertainty in electricity and water demands.
Bibliography:USDOE
SC0014114
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2017.01.021