Best-Worst Disaggregation: An Approach to the Preference Disaggregation Problem

This article 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. An optimization model is formulated to determine the most suitable additive value function that fits the preferences expressed by a decision-maker or expert. The method incorporates a consistency analysis to quantify and improve the reliability of the judgments and is extended to handle interval-valued preferences. 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. By coupling a structured human-elicitation protocol with an inference engine and a consistency-feedback loop, BWD provides a human-centered building block for evidence-sensitive decision-support systems.