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Virtual interpolation of discrete multi-objective programming solutions with probabilistic operation

Virtual interpolation of discrete multi-objective programming solutions with probabilistic operation
Virtual interpolation of discrete multi-objective programming solutions with probabilistic operation
This work presents a novel framework to address the long term operation of a class of multi-objective programming problems. The proposed approach considers a stochastic operation and evaluates the long term average operating costs/profits. To illustrate the approach, a two-phase method is proposed which solves a prescribed number of K monoobjective problems to identify a set of K points in the Paretooptimal region. In the second phase, one searches for a set of non-dominated probability distributions that define the probability that the system operates at each point selected in the first phase, at any given operation period. Each probability distribution generates a vector of average long-term objectives and one solves for the Pareto-optimal set with respect to the average objectives. The proposed approach can generate virtual operating points with average objectives that need not have a feasible solution with an equal vector of objectives. A few numerical examples are presented to illustrate the proposed method.
Discrete optimization, Dynamic operation, Pareto-optimality
0103-1759
379-389
Silva, Ricardo C.
5dafcf70-c11a-42aa-88e3-88da2b3ce63e
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Ourique, Fabrício O.
639de43f-19aa-4417-b4e4-2c7fcbefaa96
Silva, Ricardo C.
5dafcf70-c11a-42aa-88e3-88da2b3ce63e
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Ourique, Fabrício O.
639de43f-19aa-4417-b4e4-2c7fcbefaa96

Silva, Ricardo C., Arruda, Edilson F. and Ourique, Fabrício O. (2011) Virtual interpolation of discrete multi-objective programming solutions with probabilistic operation. Controle y Automacao, 22 (4), 379-389. (doi:10.1590/S0103-17592011000400005).

Record type: Article

Abstract

This work presents a novel framework to address the long term operation of a class of multi-objective programming problems. The proposed approach considers a stochastic operation and evaluates the long term average operating costs/profits. To illustrate the approach, a two-phase method is proposed which solves a prescribed number of K monoobjective problems to identify a set of K points in the Paretooptimal region. In the second phase, one searches for a set of non-dominated probability distributions that define the probability that the system operates at each point selected in the first phase, at any given operation period. Each probability distribution generates a vector of average long-term objectives and one solves for the Pareto-optimal set with respect to the average objectives. The proposed approach can generate virtual operating points with average objectives that need not have a feasible solution with an equal vector of objectives. A few numerical examples are presented to illustrate the proposed method.

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More information

Published date: July 2011
Keywords: Discrete optimization, Dynamic operation, Pareto-optimality

Identifiers

Local EPrints ID: 444717
URI: http://eprints.soton.ac.uk/id/eprint/444717
ISSN: 0103-1759
PURE UUID: 833b1807-a5ed-4f78-b6b1-ab3c1dee32d6
ORCID for Edilson F. Arruda: ORCID iD orcid.org/0000-0002-9835-352X

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Date deposited: 30 Oct 2020 17:31
Last modified: 18 Feb 2021 17:42

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