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 |
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| 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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