A Markov Chain approach to multicriteria decision analysis with an application to offshore decommissioning
A Markov Chain approach to multicriteria decision analysis with an application to offshore decommissioning
This paper proposes a novel approach that makes use of continuous-time Markov chains and regret functions to find an appropriate compromise in the context of multicriteria decision analysis (MCDA). This method was an innovation in the relationship between uncertainty and decision parameters, and it allows for a much more robust sensitivity analysis. The proposed approach avoids the drawbacks of arbitrary user-defined and method-specific parameters by defining transition rates that depend only upon the performances of the alternatives. This results in a flexible and easy-to-use tool that is completely transparent, reproducible, and easy to interpret. Furthermore, because it is based on Markov chains, the model allows for a seamless and innovative treatment of uncertainty. We apply the approach to an oil and gas decommissioning problem, which seeks a responsible manner in which to dismantle and deactivate production facilities. The experiments, which make use of published data on the decommissioning of the field of Brent, account for 12 criteria and illustrate the application of the proposed approach.
Markov chains, decommissioning, multicriteria analysis
1
Moraes, F.F.
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Ferreira Filho, Virgílio José Martins
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Infante, C.E.
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Santos, Luan
a7e5846d-25a1-4b24-aaac-2329b2be8e75
Arruda, Edilson F.
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October 2022
Moraes, F.F.
f2b25063-24ff-43a2-98d8-e5c8d377d542
Ferreira Filho, Virgílio José Martins
6853ee2e-9675-43be-afcb-77d1677716e9
Infante, C.E.
7eaaee47-c950-4f2b-8e61-b2543d481a34
Santos, Luan
a7e5846d-25a1-4b24-aaac-2329b2be8e75
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Moraes, F.F., Ferreira Filho, Virgílio José Martins, Infante, C.E., Santos, Luan and Arruda, Edilson F.
(2022)
A Markov Chain approach to multicriteria decision analysis with an application to offshore decommissioning.
Sustainability, 14 (19), , [12019].
(doi:10.3390/su141912019).
Abstract
This paper proposes a novel approach that makes use of continuous-time Markov chains and regret functions to find an appropriate compromise in the context of multicriteria decision analysis (MCDA). This method was an innovation in the relationship between uncertainty and decision parameters, and it allows for a much more robust sensitivity analysis. The proposed approach avoids the drawbacks of arbitrary user-defined and method-specific parameters by defining transition rates that depend only upon the performances of the alternatives. This results in a flexible and easy-to-use tool that is completely transparent, reproducible, and easy to interpret. Furthermore, because it is based on Markov chains, the model allows for a seamless and innovative treatment of uncertainty. We apply the approach to an oil and gas decommissioning problem, which seeks a responsible manner in which to dismantle and deactivate production facilities. The experiments, which make use of published data on the decommissioning of the field of Brent, account for 12 criteria and illustrate the application of the proposed approach.
Text
Sustainability(2022)
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Accepted/In Press date: 14 September 2022
Published date: October 2022
Additional Information:
Funding Information:
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. The authors would like to thank the Carlos Chagas Filho Foundation for Research Support in Rio de Janeiro, FAPERJ, for supporting the research by means of Grant E-26/202.789/2015. This work was partially supported by the National Council for Scientific and Technological Development—CNPq, under Grants 307126/2017-0 and 311075/2018-5.
Publisher Copyright:
© 2022 by the authors.
Keywords:
Markov chains, decommissioning, multicriteria analysis
Identifiers
Local EPrints ID: 470765
URI: http://eprints.soton.ac.uk/id/eprint/470765
ISSN: 2071-1050
PURE UUID: a886574a-18a0-4a25-a256-ea94fe27b57a
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Date deposited: 19 Oct 2022 17:01
Last modified: 17 Mar 2024 04:04
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Contributors
Author:
F.F. Moraes
Author:
Virgílio José Martins Ferreira Filho
Author:
C.E. Infante
Author:
Luan Santos
Author:
Edilson F. Arruda
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