Lexicographic optimization-based approaches to learning a representative model for multi-criteria sorting with non-monotonic criteria

TL;DR

This paper introduces lexicographic optimization methods for learning representative models in multi-criteria sorting problems involving non-monotonic criteria, addressing limitations of existing monotonic assumptions.

Contribution

It proposes novel approaches that incorporate threshold-based value-driven sorting and transformation functions to handle non-monotonic criteria in MCS models.

Findings

Effective modeling of non-monotonic criteria demonstrated

Optimization models rectify preference inconsistencies

Simulation confirms approach validity

Abstract

Deriving a representative model using value function-based methods from the perspective of preference disaggregation has emerged as a prominent and growing topic in multi-criteria sorting (MCS) problems. A noteworthy observation is that many existing approaches to learning a representative model for MCS problems traditionally assume the monotonicity of criteria, which may not always align with the complexities found in real-world MCS scenarios. Consequently, this paper proposes some approaches to learning a representative model for MCS problems with non-monotonic criteria through the integration of the threshold-based value-driven sorting procedure. To do so, we first define some transformation functions to map the marginal values and category thresholds into a UTA-like functional space. Subsequently, we construct constraint sets to model non-monotonic criteria in MCS problems and develop optimization models to check and rectify the inconsistency of the decision maker's assignment example preference information. By simultaneously considering the complexity and discriminative power of the models, two distinct lexicographic optimization-based approaches are developed to derive a representative model for MCS problems with non-monotonic criteria. Eventually, we offer an illustrative example and conduct comprehensive simulation experiments to elaborate the feasibility and validity of the proposed approaches.