Lexicographic optimization-based approaches to learning a representative model for multi-criteria sorting with non-monotonic criteria
This addresses a limitation in multi-criteria sorting for decision-making scenarios where criteria are not monotonic, though it appears incremental as it extends existing value function-based methods.
The paper tackles the problem of learning a representative model for multi-criteria sorting with non-monotonic criteria, which existing methods often assume are monotonic, by proposing lexicographic optimization-based approaches that integrate threshold-based value-driven sorting and demonstrate feasibility and validity through simulation experiments.
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.