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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| Vydáno v: | Computers & chemical engineering Ročník 99; číslo C; s. 185 - 197 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United Kingdom
Elsevier Ltd
06.04.2017
Elsevier |
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| ISSN: | 0098-1354, 1873-4375 |
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| Abstract | •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. |
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| AbstractList | •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. |
| Author | Fuentes-Cortes, Luis Fabian Cao, Yankai Chen, Siyu Zavala, Victor M. |
| Author_xml | – sequence: 1 givenname: Yankai surname: Cao fullname: Cao, Yankai organization: Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr, Madison, WI 53706, USA – sequence: 2 givenname: Luis Fabian surname: Fuentes-Cortes fullname: Fuentes-Cortes, Luis Fabian organization: Department of Chemical Engineering, Universidad Michoacana de San Nicolas de Hidalgo, Morelia, Michoacan 58060, Mexico – sequence: 3 givenname: Siyu surname: Chen fullname: Chen, Siyu organization: Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr, Madison, WI 53706, USA – sequence: 4 givenname: Victor M. surname: Zavala fullname: Zavala, Victor M. email: victor.zavala@wisc.edu organization: Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr, Madison, WI 53706, USA |
| BackLink | https://www.osti.gov/biblio/1398714$$D View this record in Osti.gov |
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