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

Author(s)

de Moraes, Matheus Bernardelli ; Coelho, Guilherme Palermo ; Bratvold, Reidar B.

• Document type: Journal article
• Source: DECISION ANALYSIS; v. N/A, p. 18-pg., 2025-01-13.

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. (AU)

• FAPESP's process: 17/15736-3 - Engineering Research Centre in Reservoir and Production Management
• Grantee: Denis José Schiozer
• Support Opportunities: Research Grants - Research Centers in Engineering Program