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

Matheus Bernardelli de Moraes (), Guilherme Palermo Coelho () and Reidar B. Bratvold ()
Additional contact information
Matheus Bernardelli de Moraes: Faculdade de Tecnologia (FT), Universidade Estadual de Campinas (UNICAMP), Limeira 13484-332, Brazil
Guilherme Palermo Coelho: Faculdade de Tecnologia (FT), Universidade Estadual de Campinas (UNICAMP), Limeira 13484-332, Brazil
Reidar B. Bratvold: Department of Energy Resources, University of Stavanger (UiS), 4036 Stavanger, Norway

Decision Analysis, 2025, vol. 22, issue 3, 189-205

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.

Keywords: energy; decision analysis; decision making under uncertainty; decision trees; environment (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/deca.2024.0188 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ordeca:v:22:y:2025:i:3:p:189-205

Access Statistics for this article

More articles in Decision Analysis from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().