Dimensionality reduction for multi-criteria problems: An application to the decommissioning of oil and gas installations

•Assessing criteria is essential for decision making in decommissioning.•Large oil and gas fields require a large number of criteria evaluations.•We propose feature selection and classification methods for dimensionality reduction.•The dataset is composed by a reduced set of sub-criteria and paramet...

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Bibliographic Details
Published in:	Expert systems with applications Vol. 148; p. 113236
Main Authors:	Martins, Isabelle D., Bahiense, Laura, Infante, Carlos E.D., Arruda, Edilson F.
Format:	Journal Article
Language:	English
Published:	New York Elsevier Ltd 15-06-2020 Elsevier BV
Subjects:	Artificial intelligence Decommissioning Dimensionality reduction Feature selection Machine learning Multi-criteria decision analysis Multiple criterion Oil and gas Training Dimensionality reduction Feature selection Oil and gas Decommissioning Multi-criteria decision analysis Machine learning
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Summary:	•Assessing criteria is essential for decision making in decommissioning.•Large oil and gas fields require a large number of criteria evaluations.•We propose feature selection and classification methods for dimensionality reduction.•The dataset is composed by a reduced set of sub-criteria and parameters.•Significant reduction in dimension can have little impact on performance. This paper is motivated by decommissioning studies in the field of oil and gas, which comprise a very large number of installations and are of interest to a large number of stakeholders. Generally, the problem gives rise to complicated multi-criteria decision aid tools that rely upon the costly evaluation of multiple criteria for every piece of equipment. We propose the use of machine learning techniques to reduce the number of criteria by feature selection, thereby reducing the number of required evaluations and producing a simplified decision aid tool with no sacrifice in performance. In addition, we also propose the use of machine learning to explore the patterns of the multi-criteria decision aid tool in a training set. Hence, we predict the outcome of the analysis for the remaining pieces of equipment, effectively replacing the multi-criteria analysis by the computational intelligence acquired from running it in the training set. Computational experiments illustrate the effectiveness of the proposed approach.
ISSN:	0957-4174 1873-6793
DOI:	10.1016/j.eswa.2020.113236