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

Matheus Bernardelli de Moraes et al.

Decision Analysis2025https://doi.org/10.1287/deca.2024.0188article
AJG 1ABDC A
Weight
0.37

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].

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https://doi.org/https://doi.org/10.1287/deca.2024.0188

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@article{matheus2025,
  title        = {{A Machine Learning-Assisted Decision-Making Methodology Based on Simplex Weight Generation for Non-Dominated Alternative Selection}},
  author       = {Matheus Bernardelli de Moraes et al.},
  journal      = {Decision Analysis},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1287/deca.2024.0188},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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