3MO-AHP: an inconsistency reduction approach through mono-, multi- or many-objective quality measures. (18th February 2022)

• Record Type: Journal Article
• Authors: Floriano, Carla Martins; Pereira, Valdecy; Rodrigues, Brunno e Souza
• Is Part Of: Data technologies and applications. Volume 56:Number 5(2022)
• Page Start: 645
• Page End: 670
• Publication Date: 2022-02-18
• Subjects: Analytic hierarchy process -- Inconsistency reduction -- Many-objective optimization algorithm -- U-NSGA III; Information science -- Periodicals; Electronic information resources -- Periodicals; Knowledge management -- Periodicals; 020.5
• DOI: 10.1108/DTA-11-2021-0315
• Languages: English
• ISSNs: 2514-9288

Abstract

Abstract : Purpose: Although the multi-criteria technique analytic hierarchy process (AHP) has successfully been applied in many areas, either selecting or ranking alternatives or to derive priority vector (weights) for a set of criteria, there is a significant drawback in using this technique if the pairwise comparison matrix (PCM) has inconsistent comparisons, in other words, a consistency ratio (CR) above the value of 0.1, the final solution cannot be validated. Many studies have been developed to treat the inconsistency problem, but few of them tried to satisfy different quality measures, which are minimum inconsistency (f MI ), the total number of adjusted pairwise comparisons (f NC ), original rank preservation (f KT ), minimum average weights adjustment (f WA ) and finally, minimum L1 matrix norm between the original PCM and the adjusted PCM (f LM ). Design/methodology/approach: The approach is defined in four steps: first, the decision-maker should choose which quality measures she/he wishes to use, ranging from one to all quality measures. In the second step, the authors encode the PCM to be used in a many-objective optimization algorithm (MOOA), and each pairwise comparison can be adjusted individually. The authors generate consistent solutions from the obtained Pareto optimal front that carry the desired quality measures in the third step. Lastly, the decision-maker selects the most suitable solution for her/his problem. Remarkably, as the decision-maker can choose one (mono-objective), two (multi-objective), three or more (many-objectives) quality measures, not all MOOAs can handle or perform well in mono- or multi-objective problems. The unified non-sorting algorithm III (U-NSGA III) is the most appropriate MOOA for this type of scenario because it was specially designed to handle mono-, multi- and many-objective problems. Findings: The use of two quality measures should not guarantee that the adjusted PCM is similar to the original PCM; hence, the decision-maker should consider using more quality measures if the objective is to preserve the original PCM characteristics. Originality/value: For the first time, a many-objective approach reduces the CR to consistent levels with the ability to consider one or more quality measures and allows the decision-maker to adjust each pairwise comparison individually.