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Application of the method of data reconciliation for minimizing uncertainty of the weight function in the multicriteria optimization model

Application of the method of data reconciliation for minimizing uncertainty of the weight... Abstract The multicriteria decision process consists of five main steps: definition of the optimisation problem, determination of the weight structure of the decision criteria, design of the evaluation matrix, selection of the optimal evaluation method and ranking of solutions. It is often difficult to obtain the optimal solution to a multicriterion problem. The main reason is the subjective element of the model – the weight functions of the decision criteria. Expert opinions are usually taken into account in their determination. The aim of this article is to present a novel method of minimizing the uncertainty of the weights of the decision criteria using Monte Carlo simulation and method of data reconciliation. The proposed method is illustrated by the example of multicriterion social effectiveness evaluation for electric power supply to a building using renewable energy sources. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Thermodynamics de Gruyter

Application of the method of data reconciliation for minimizing uncertainty of the weight function in the multicriteria optimization model

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Publisher
de Gruyter
Copyright
Copyright © 2015 by the
ISSN
2083-6023
eISSN
2083-6023
DOI
10.1515/aoter-2015-0006
Publisher site
See Article on Publisher Site

Abstract

Abstract The multicriteria decision process consists of five main steps: definition of the optimisation problem, determination of the weight structure of the decision criteria, design of the evaluation matrix, selection of the optimal evaluation method and ranking of solutions. It is often difficult to obtain the optimal solution to a multicriterion problem. The main reason is the subjective element of the model – the weight functions of the decision criteria. Expert opinions are usually taken into account in their determination. The aim of this article is to present a novel method of minimizing the uncertainty of the weights of the decision criteria using Monte Carlo simulation and method of data reconciliation. The proposed method is illustrated by the example of multicriterion social effectiveness evaluation for electric power supply to a building using renewable energy sources.

Journal

Archives of Thermodynamicsde Gruyter

Published: Mar 1, 2015

References