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  • 1-s2.0-S0305048317300051-main

    Rights statement: This is the author’s version of a work that was accepted for publication in Omega. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Omega, ??, ??, 2017 DOI: 10.1016/j.omega.2017.05.009

    Accepted author manuscript, 691 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

  • A Bayesian approach to multicriteria decision making

    Rights statement: This is the author’s version of a work that was accepted for publication in Omega. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Omega, ??, ??, 2017 DOI: 10.1016/j.omega.2017.05.009

    Accepted author manuscript, 389 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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A Bayesian approach to find Pareto optima in multiobjective programming problems using Sequential Monte Carlo algorithms

Research output: Contribution to Journal/Magazine › Journal article › peer-review

E-pub ahead of print
<mark>Journal publication date</mark>3/06/2017
<mark>Journal</mark>Omega: The International Journal of Management Science
Publication StatusE-pub ahead of print
Early online date3/06/17
<mark>Original language</mark>English

Abstract

In this paper we consider a new approach to multicriteria decision making problems. Such problems are, usually, cast into a Pareto framework where the objective functions are aggregated into a single one using certain weights. The problem is embedded into a statistical framework by adopting a posterior distribution for both the decision variables and the Pareto weights. This embedding dates back to [25] but in this work we operationalize the concept further. We propose a Metropolis-Hastings and a Sequential Monte Carlo (SMC) to trace out the entire Pareto frontier and / or find the global optimum of the problem. We apply the new techniques to a multicriteria portfolio decision making problem proposed in [37] and to a test problem proposed by [27]. The good performance of new techniques suggests that SMC and other algorithms, like the classical Metropolis-Hastings algorithm, can be used profitably in the context of multicriteria decision making problems to trace out the Pareto frontier and / or find a global optimum. Most importantly SMC can be considered as an off-the-shelf technique to solve arbitrary multicriteria decision making problems routinely and efficiently.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Omega. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Omega, ??, ??, 2017 DOI: 10.1016/j.omega.2017.05.009