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A Machine Learning-Assisted Decision-Making Methodology Based on Simplex Weight Generation for Non-Dominated Alternative Selection.

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Authors: de Moraes, Matheus Bernardelli1 (AUTHOR) mbmoraes@unicamp.br, Coelho, Guilherme Palermo1 (AUTHOR) gpcoelho@unicamp.br, Bratvold, Reidar B.2 (AUTHOR) reidar.bratvold@uis.no
Source: Decision Analysis (INFORMS). Sep2025, Vol. 22 Issue 3, p189-205. 17p.
Subject Terms: *Decision making, *Multiple criteria decision making, *Quantitative research, *Petroleum industry, Machine learning, Multi-objective optimization
Abstract: In multiobjective decision-making problems, it is common to encounter nondominated alternatives. In these situations, the decision-making process becomes complex, as each alternative offers better outcomes for some objectives and worse outcomes for others simultaneously. However, DMs still must choose a single alternative that provides an acceptable balance between the conflicting objectives, which can become exceedingly challenging. To address this scenario, our work introduces a decision-making framework aimed at supporting such decisions. Our proposed framework draws upon concepts from the field of Multi-Criteria Decision Making, and combines a novel simplex-like weight generation method with expert insights and machine learning data-driven procedures to establish an intuitive methodology that empowers DMs to select a single alternative from a range of alternatives. In this paper, we illustrate the effectiveness of our methodology through an example and two real-world decision cases from the oil and gas industry, each involving 128 alternatives and five distinct objectives. Funding: This work was supported by Equinor [Grant 2017/15736-3]; Fundação de Amparo à Pesquisa do Estado de São Paulo [Grant 2017/15736-3]. [ABSTRACT FROM AUTHOR]
Database: Entrepreneurial Studies Source
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  Data: <searchLink fieldCode="JN" term="%22Decision+Analysis+%28INFORMS%29%22">Decision Analysis (INFORMS)</searchLink>. Sep2025, Vol. 22 Issue 3, p189-205. 17p.
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  Data: *<searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br />*<searchLink fieldCode="DE" term="%22Multiple+criteria+decision+making%22">Multiple criteria decision making</searchLink><br />*<searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink><br />*<searchLink fieldCode="DE" term="%22Petroleum+industry%22">Petroleum industry</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink>
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  Data: In multiobjective decision-making problems, it is common to encounter nondominated alternatives. In these situations, the decision-making process becomes complex, as each alternative offers better outcomes for some objectives and worse outcomes for others simultaneously. However, DMs still must choose a single alternative that provides an acceptable balance between the conflicting objectives, which can become exceedingly challenging. To address this scenario, our work introduces a decision-making framework aimed at supporting such decisions. Our proposed framework draws upon concepts from the field of Multi-Criteria Decision Making, and combines a novel simplex-like weight generation method with expert insights and machine learning data-driven procedures to establish an intuitive methodology that empowers DMs to select a single alternative from a range of alternatives. In this paper, we illustrate the effectiveness of our methodology through an example and two real-world decision cases from the oil and gas industry, each involving 128 alternatives and five distinct objectives. Funding: This work was supported by Equinor [Grant 2017/15736-3]; Fundação de Amparo à Pesquisa do Estado de São Paulo [Grant 2017/15736-3]. [ABSTRACT FROM AUTHOR]
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      – Type: doi
        Value: 10.1287/deca.2024.0188
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      – Code: eng
        Text: English
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      – SubjectFull: Multiple criteria decision making
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      – SubjectFull: Quantitative research
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      – SubjectFull: Petroleum industry
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      – SubjectFull: Machine learning
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      – SubjectFull: Multi-objective optimization
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      – TitleFull: A Machine Learning-Assisted Decision-Making Methodology Based on Simplex Weight Generation for Non-Dominated Alternative Selection.
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              M: 09
              Text: Sep2025
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