Cloud-f-divergence based probabilistic hesitant fuzzy multi-criteria sorting method: An application to medical insurance fraud

A multi-criteria sorting method for preference maps based on Nash-Stackelberg game

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

A novel multi-criteria sorting method based on the linguistic polyhedral hesitant fuzzy consensus-reaching model

Preference learning for multi-criteria sorting with interacting criteria: A framework integrating threshold-based value-driven sorting procedure with attention network

An ExpTODIM based multi-criteria sorting method under uncertainty

Spatial multicriteria sorting with intuitionistic fuzzy entropy for the hospitality sector

The exploitation of non timber forest products (NTFPs), are the main source of income for rural populations in the Pô region of Burkina Faso. However, the potential of NTFP-providing species is not always exploited in a way that provides greater profitability for rural populations. This is mainly due to the fact that several criteria must be taken into account and aggregated in agreement with the various stakeholders. The aim of this work is to propose a prioritization of NTFP-providing species based on their ability to provide greater profitability for local populations. To this end, a proposition of a methodology based on the joint use of the KEMIRA-sort and ELECTRE Tri multi-criteria sorting methods to assign NTFP-providing species to ordered categories of profitability is made. The application of this methodology enabled to identify a median categorization made up of three categories of NTFP-providing species according to their profitability in the Pô area. The best category is made up of one specie (tamarind), while the second best category is made up of four species (shea, baobab, moringa, mango) and the last of five species (nere, detarium, jujube, resin and liana). The concordance of these results with the estimate of the expert, playing the role of decision maker, at the very start of our study, validate empirically the proposed approach, prefiguring its successful application to other context of multiple criteria sorting problems in the future (e.g. prioritizing areas at risk of the spread of a given disease in public health). Our approach also shows the complementary use of a total aggregation method (KEMIRA-sort) and an outranking method (ELECTRE Tri) in solving a multi-criteria sorting problem.

ABSTRACT Inventory classification in make-to-order (MTO) manufacturing requires more than traditional ABC methods, which focus mainly on cost or consumption. In environments with high customization and dynamic production, multi-criteria inventory classification offers a broader perspective by considering multiple internal and external factors. In this regard, current literature offers an array of approaches that extend multi-criteria decision-making methods to handle multi-criteria sorting problems. This study contributes to the discussion by introducing EDAS-Sort-C, a novel method that combines Evaluation based on Distance from Average Solution (EDAS) with ABC classification. Instead of limiting profiles, EDAS-Sort-C employs central profiles for each class as averages and then evaluates inventory items based on the computational framework of EDAS. Within each class, items are also ranked by importance to facilitate the identification of more critical items. A case study in an MTO furniture firm, using six criteria and 157 inventory items, confirms the effectiveness of the proposed EDAS-Sort-C.

An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting

ABSTRACT This work proposes a novel multi-criteria sorting approach for evaluating the compliance of road signs based on ergonomic principles and sign comprehension using an integrated Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) sorting (TOPSIS-Sort) under an environment that handles uncertainty via q -rung orthopair fuzzy sets ( q -ROFS). The q -ROF-CRITIC assigns the priority weights of the attributes (i.e. comprehension and ergonomic principles), whereas the q -ROF-TOPSIS-Sort evaluates and classifies the compliance levels of road signs in view of a set of pre-defined categories, consequently bridging the limitations of the TOPSIS-Sort in handling imprecise evaluations. Demonstrated in an actual case study of evaluating 83 road signs in the Philippines, results show that prohibitive signs have the highest comprehension levels, while parking and stop signs, together with road obstacle signs, belong to the medium compliance level. Low-level compliance is observed for supplementary and intersection road signs due to unfamiliarity, while horizontal signs are ergonomically low on spatial and physical attributes. The proposed approach is supported by sensitivity analysis of q values and comparative assessments with other methods. The findings encourage further investigation into comprehensibility evaluations and open avenues for exploring the factors that influence road sign comprehension.

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

Assessment of digital economy development with the new multicriteria sorting method: DCMSort

Strategic behavior in multi-criteria sorting with trust relationships-based consensus mechanism: Application in supply chain risk management

Integrating machine learning models to learn potentially non-monotonic preferences for multi-criteria sorting from large-scale assignment examples

This work offers an integrated methodological framework that integrates the capabilities of large language models (LLMs), rules-based reasoning, multi-criteria sorting, and artificial neural networks (ANN) in developing a predictive model for classifying the intensity of sensitive social media contents. The current literature lacks a holistic consideration of multiple attributes in evaluating social media contents, and the proposed framework intends to bridge such a gap. Three actions constitute the development of the framework. First, LLMs (i.e., GPT4) evaluate the social media contents under a predefined set of attributes, leveraging the power of LLMs in content analytics. Second, rules-based reasoning and multi-criteria sorting (i.e., entropy-FlowSort) determine the categories of social media contents. Lastly, the two previous actions produced a complete dataset that can be used to train a predictive model using ANN to classify sensitive social media contents. With 1100 randomly extracted social media contents and the predefined categories of violations against community standards set by Facebook, the proposed integrated methodology produces an ANN-based classification model with 86.36% prediction accuracy. Comparative analysis using Decision Trees, k-nearest neighbors, Linear Discriminant Analysis, Random Forest, and Naive Bayes classification yields the highest performance of ANN. The predictive model can be used as a decision-support tool to design moderation actions on social media contents.

The handling of missing attribute values remains a challenging and problematic issue in data analysis. Imputation techniques are key procedures used to deal with missing attribute values. However, although these methods are widely used, they cause data bias. Rough set theory, a unique mathematical tool for decision making under uncertainty, overcomes this problem by properly adjusting the relationships. Rough sets are often preferred in both classification and sorting problems. The aim of sorting problems is to sort the objects in the decision table (DT) from best to worst and/or to select the best one. For this purpose, it is necessary to obtain a pairwise comparison table (PCT) from the DT. However, in the presence of missing values, the transformation from DT to PCT is not feasible because there are no ranking methods in the literature for sorting problems based on rough sets. To address this limitation, this paper presents a way to transform from DT to PCT and introduces a generalization of the relation belonging to the “do not care” type of missing values in the dominance-based rough set approach (DRSA) to the decision support tool jRank. We also adapted the DomLem algorithm to enable it to work in PCT with missing values. We applied our method step by step to a decision table with 11 objects and investigated the effect of missing values. The experimental results showed that our proposed approach captures the semantics of ‘do not care’ type missing values.

ABSTRACT Drought is a natural phenomenon that poses a significant threat to water resources in affected regions. The detrimental effects of this extreme weather event, regardless of its type, have had an impact, necessitating concrete resource allocation projects to mitigate drought. From this perspective, our research proposes a group multicriteria sorting model to support resource allocation for drought mitigation. A multicriteria sorting model was developed based on the PROMSORT method to assist the government in evaluating 14 municipalities of the Apodi-Mossoró river basin in the Rio Grande do Norte State in Brazil. This model classifies the municipalities into high, moderate, and low-priority categories, enabling targeted attention and allocation of resources for drought mitigation efforts. The research findings demonstrate that the proposed model can effectively support strategic public policies, allocate resources, and facilitate the implementation of appropriate actions, thereby focusing efforts on the cities most severely affected by drought and alleviating the adverse effects of this natural disaster

An adaptive consensus model for multi-criteria sorting under linguistic distribution group decision making considering decision-makers’ attitudes

A semi-supervised multi-criteria sorting approach to constructing social vulnerability composite indicators