“From Intuition to Optimization: A Hybrid FAHP-MAUT Model for Informed R&D Investment Decision in Mining”

Abstract

The mining industry has traditionally relied on personal experience and intuition for decision-making since industry managers and leaders are faced with uncertainty, diverse options, and limited resources to make a more objective and rational decision. Multicriteria decision analysis (MCDA) techniques have been introduced to address this challenge, yet the existing methods often focus on simplicity rather than optimality. Therefore, this research aims to develop a hybrid model that combines fuzzy analytical hierarchy process (FAHP) with multi-attribute utility theory (MAUT) to help decision-makers achieve optimal results when faced with diverse investment opportunities and criteria. The study uses data from a private consulting firm. The report consists of 225 projects and 16 attributes. The statistical analysis was performed through SPSS, involving a t-test, one-way ANOVA, Pearson correlation, and regression analysis. FAHP and MAUT were performed via python programming and a sensitivity analysis was conducted to verify the validity of the data. The results demonstrate that the developed model can be utilized as a tool to mitigate subjectivity and provide a more objective and reliable ranking even in the long term. It also highlights the correlation between selected attributes and the context of investment opportunities. Attributes alone are necessary but not sufficient to influence rankings holistically. Ultimately, the study’s findings shed light on the interplay between attributes and investment contexts, emphasizing their interdependence. By adopting this uncommon model as a tool, decision-makers can make more informed choices and enhance their decision-making processes in the mining industry and other sectors.