Leveraging preference disaggregation for context-dependent adaptive multi-criteria sorting with incomplete information

Balancing production and environmental protection is a core challenge in mining activities, where heavy metal discharge may cause water pollution. This calls for effective environmental risk management based on pollution assessments that identify high-risk cases and provide actionable insights. Given the high cost of water quality tests, the predictive identification of high-risk samples based on historical data can streamline pollution assessments and enable the prioritisation of monitoring resources. However, the sporadic nature of mining-related pollution events may lead to scarce historical data, making predictive modelling difficult. To address data scarcity, this study develops a convex quadratic preference disaggregation model that reformulates pointwise supervision into relational supervision using the additive fuzzy preference structure. The model relaxes parametric assumptions incompatible with pollution assessment through piecewise linear approximations of nonlinear risk preferences, and supports both relative and absolute predictive outputs to suit diverse decision scenarios. A case study of mine-derived water pollution assessment with scarce data and experiments on standard datasets demonstrate the effectiveness and applicability of the method. Environmental management insights regarding sample screening, risk stratification, and deferred decision-making are provided.

Multi-period medical user generated content fusion for physician rating based on preference disaggregation

Modeling the selection of representative aggregation functions for optimizing the representation of group behavior preferences within the preference disaggregation framework

This study develops a data-driven framework integrating natural language processing (NLP) and a Bayesian preference disaggregation model to rank physicians using user-generated content (UGC) on medical platforms. Previous studies failed to extract diverse decision-making criteria and corresponding values accurately and neglected criteria interactions in extracting user preferences. To address these gaps, a pre-trained Chinese-RoBERTa model is fine-tuned to accurately compute sentiment polarities from text reviews, while a qualifier corpus is developed to assess sentiment intensities. The extracted decision information includes crisp values, interval values, and probabilistic linguistic term sets. To model this heterogeneous information, a Bayesian preference disaggregation model is designed to disaggregate pairwise comparisons of alternatives, deriving value functions that capture both marginal and interaction utilities of criteria. The Metropolis-Hasting algorithm is employed to solve the model, and the inferred value functions are used to rank physicians. A case study utilizing physician reviews from haodf.com demonstrates the effectiveness of the proposed framework, with comparative analysis of two case studies against Bayesian ordinal regression and stochastic ordinal regression methods further validating its performance.

Multi-criteria consensus sorting model with flexible linguistic preferences based on fuzzy information granulation from the perspective of preference disaggregation

Preference disaggregation analysis with criteria selection in a regularization framework

An enhanced failure mode and effect analysis method based on preference disaggregation in risk analysis of intelligent wearable medical devices

Improving preference disaggregation in multicriteria decision making: Incorporating time series analysis and a multi-objective approach

In the context of big data and artificial intelligence, analyzing and extracting actionable insights from extensive datasets to enhance decision-making processes presents both intriguing opportunities and formidable challenges. Existing multiple criteria sorting (MCS) methodologies often struggle with the magnitude of these datasets, particularly in terms of time and memory requirements. Furthermore, traditional approaches typically rely on direct preference information, which can be cognitively demanding for decision-makers and may not scale effectively with increasing data complexity. This study introduces a scalable MCS approach grounded in the MapReduce framework, designed to handle extensive sets of alternatives and preference information in a parallel processing paradigm. The proposed approach utilizes an additive piecewise-linear value function as the underlying preference model, with model parameters inferred from assignment examples on a subset of reference alternatives through the application of preference disaggregation principles. To enable the parallel execution of the sorting procedure, a convex optimization model is formulated to estimate the parameters of the preference model. Subsequently, a parallel algorithm is devised to solve this optimization model, leveraging the MapReduce framework to process the set of reference alternatives and associated preference information concurrently, thereby accelerating computational efficiency. Additionally, the performance of the proposed approach is evaluated using a real-world dataset and a series of synthetic datasets comprising up to 400,000 alternatives. The findings demonstrate that this approach effectively addresses the MCS problem in the context of large sets of alternatives and extensive preference information.

In the field of linguistic decision making, it is widely acknowledged that different individuals may have different understandings of the same linguistic information. Consequently, the modeling of personalized individual semantics (PISs) has emerged as a prominent research avenue within the domain of linguistic decision making. In this article, we study multicriteria decision making problems with incomplete linguistic preference relations (ILPRs), particularly in scenarios where the marginal utility function of each criterion remain elusive. We develop a new approach for modeling PISs of decision makers from the preference disaggregation perspective. The proposed method initiates by representing the marginal utility of each alternative regarding to every criterion, employing piecewise linear functions. It then customizes linguistic preference values within an ILPR by translating them into numerical preference intensities. Subsequently, this article formulates optimization models to check and rectify the inconsistencies between the multicriteria assessment information and the known elements in the ILPR. Furthermore, an optimization model is devised with the objective of minimizing the inconsistency index of the complete linguistic preference relation, thereby enabling the estimation of missing elements of the ILPR and the ranking of alternatives. Consequently, this method also facilitates the derivation of marginal utility functions and PISs for the decision maker. The proposed method is illustrated using a practical scenario involving a car purchase problem, substantiated by a series of simulation experiments and comparative analysis, ultimately providing validation for the proposed approach.

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

Navigating ESG complexity: An in-depth analysis of sustainability criteria, frameworks, and impact assessment

Agricultural investment project selection is a complex multi-criteria decision-making problem, as agricultural projects are easily influenced by various risk factors, and the evaluation information provided by decisionmakers usually involves uncertainty and inconsistency. Existing literature primarily employed direct preference elicitation methods to address such issues, necessitating a great cognitive effort on the part of decision-makers during evaluation, specifically, determining the weights of criteria. In this study, we propose an indirect preference elicitation method, known as a preference disaggregation method, to learn decision-maker preference models from decision examples. To enhance evaluation ease, decision-makers merely need to compare pairs of alternatives with which they are familiar, also known as reference alternatives. Probabilistic linguistic preference relations are employed to account for the presence of incomplete and uncertain information in such pairwise comparisons. To address the inconsistency among a group of decision-makers, we develop a pair of 0-1 mixed integer programming models that consider both the semantics of linguistic terms and the belief degrees of decision-makers. Finally, we conduct a case study and comparative analysis. Results reveal the effectiveness of the proposed model in solving agricultural investment project selection problems with uncertain and inconsistent decision information.

To mitigate power supply and demand imbalances and achieve carbon reduction goals, provinces in China have recently been promoting the development of renewable energy (RE) projects. Multiple criteria analysis is commonly employed for RE project assessment as energy considerations from multiple perspectives are required. To support governments’ reserve work on RE projects and facilitate their smooth development, this study introduces an interactive preference disaggregation approach for RE project sorting. The sorting model accounts for the influence of different experts. To resolve contradictory preferences among experts, mathematical programming models that can ensure the reliability of the decision-making results are constructed for conflicting analysis and adjustment strategy recommendations. The proposed approach achieves a certain form of “cognitive convergence” through iterative processes and finally derives sorting results aligning with the perceptions of all experts. In addition, to promote the high-quality development of RE projects, an optimization model is given to generate improvement strategies for inferior projects based on the sorting results. An illustrative example of solar photovoltaic project sorting is provided to validate the applicability of the proposed approach.

A preference disaggregation based multi-criteria satisfaction degree evaluation model

Preference disaggregation analysis for sorting problems in the context of group decision-making with uncertain and inconsistent preferences

We consider preference disaggregation in the context of multiple criteria sorting. The value function parameters and thresholds separating the classes are inferred from the Decision Maker’s (DM’s) assignment examples. Given the multiplicity of sorting models compatible with indirect preferences, selecting a single, representative one can be conducted differently. We review several procedures for this purpose, aiming to identify the most discriminant, average, central, parsimonious, or robust models. Also, we present three novel procedures that implement the robust assignment rule in practice. They exploit stochastic acceptabilities and maximize the support given to the resulting assignments by all feasible sorting models. The performance of fourteen procedures is verified on problem instances with different complexities. The results of an experimental study indicate the most efficient procedures in terms of classification accuracy, reproducing the DM’s model, and delivering the most robust assignments. These include approaches identifying differently interpreted centers of the feasible polyhedron and robust methods introduced in this paper. Moreover, we discuss how the performance of all procedures is affected by different numbers of classes, criteria, characteristic points, and reference assignments.

A compensatory value function for modeling risk tolerance and criteria interactions in preference disaggregation

Consumer preference disaggregation based on online reviews to support new energy automobile purchase decision