Best-Worst Disaggregation: An approach to the preference disaggregation problem

TL;DR

The paper introduces the Best-Worst Disaggregation (BWD) method, which improves preference modeling in MCDM by reducing cognitive biases and enhancing consistency through a novel pairwise comparison approach.

Contribution

It presents a new BWD method integrating Best-Worst principles into disaggregation, including an optimization model, consistency analysis, and extension to interval preferences.

Findings

BWD yields more consistent and reliable preference models.

The method effectively handles uncertainty with interval preferences.

Case study confirms BWD's practical applicability and improved ranking accuracy.

Abstract

Preference disaggregation methods in Multi-Criteria Decision-Making (MCDM) often encounter challenges related to inconsistency and cognitive biases when deriving a value function from experts' holistic preferences. This paper introduces the Best-Worst Disaggregation (BWD) method, a novel approach that integrates the principles of the Best-Worst Method (BWM) into the disaggregation framework to enhance the consistency and reliability of derived preference models. BWD employs the "consider-the-opposite" strategy from BWM, allowing experts to provide two opposite pairwise comparison vectors of alternatives. This approach reduces cognitive load and mitigates anchoring bias, possibly leading to more reliable criteria weights and attribute value functions. An optimization model is formulated to determine the most suitable additive value function to the preferences expressed by an expert. The method also incorporates a consistency analysis to quantify and improve the reliability of the judgments. Additionally, BWD is extended to handle interval-valued preferences, enhancing its applicability in situations with uncertainty or imprecise information. We also developed an approach to identify a reference set, which is used for pairwise comparisons to elicit the value functions and weights. A case study in logistics performance evaluation demonstrates the practicality and effectiveness of BWD, showing that it produces reliable rankings aligned closely with experts' preferences.