Outranking Relation Research Articles

Personnel Selection in a Coffee Shop Company Based on a Multi-Criteria Decision-Aiding and Artificial Intelligence Approach

Human capital management is a strategic element for companies in a globalized world. Therefore, they must use strategies and methods to recruit and select personnel assertively to focus their training, strengthening, and business growth efforts. Personnel selection can be seen as a decision problem and can be addressed in a multi-criteria decision-making context. This work aims to present the selection process of a barista in a Mexican coffee shop. The baristas could be the face of the company to customers, and they could significantly impact their overall experience. The personnel selection process included eleven candidates and three criteria. This process was performed using the ELECTRE-III to model the preferences of a decision-maker and RP2-NSGA-II+H, a multi-objective evolutionary algorithm that exploits fuzzy outranking relations to derive multi-criteria rankings. The ordering obtained with the algorithm did not have any inconsistency concerning the integral preference model, and it allowed for the selection of a candidate to occupy the barista position. The results show the relevance of combining preference modeling with multi-criteria analysis methods for decision-making and artificial intelligence techniques.

Outranking-based approaches for multiple criteria partially ordered clustering: A review of existing algorithms, new proposals, and experimental comparison

We consider clustering problems that involve categorizing alternatives into partially ordered, initially undefined groups based on their performance across multiple criteria. To achieve this, we use an outranking relation model to reflect the Decision Maker's preferences. We examine various algorithms that not only group the alternatives but also order the clusters in different ways. This analysis includes innovative approaches that use distances in the space of outranking relations or detailed relation profiles, and apply orthogonal non-negative factorization to outranking matrices. Additionally, we discuss a set of measures, including two novel ones, for assessing the effectiveness of clustering when the groupings are partially ordered. Our findings are based on comprehensive computational experiments on real-world and simulated datasets. Beyond evaluating various methods using four quality metrics and computational efficiency, we explore the influence of accessible preference structures and ordering techniques on the clustering outcomes.

Multi-attribute three-way decision making based on interval-valued intuitionistic fuzzy sets

In the interval-valued intuitionistic fuzzy environment, a new multi-attribute three-way decision-making model is proposed to address the problems that the relative loss function in the existing multi-attribute three-way decision-making model does not consider the degree of hesitancy, and the alternative conditional probabilities are given subjectively by the authors, which lacks objectivity. First, three types of ideal solutions are introduced, and the correlation coefficients between the evaluated values and ideal solutions are utilized to construct alternative relative loss functions. Second, the ELECTRE-I method is generalized to the interval-valued intuitionistic fuzzy environment to establish the outranking relation and a method for estimating the conditional probability of alternatives is given. Finally, the model is used to experimentally analyze examples to illustrate the effectiveness and rationality of the model.

Dynamic three-way multi-criteria decision making with basic uncertain linguistic information: A case study in product ranking

The concept of basic uncertain linguistic information (BULI) is proposed as an extension of basic uncertain information to enhance the measurement of data quality in decision-making processes by allowing for more flexible utilization of degrees of certainty. This paper seeks to investigate a new model of dynamic three-way decisions and develop an integrated framework for BULI-based multi-criteria decision-making using decision-theoretic rough sets (DTRSs). Firstly, we present the BULI-input dynamic ELECTRE-I method, which is capable of establishing an outranking relation. Then, a novel model of BULI decision-theoretic rough sets (BULIDTRSs) is proposed. The BULI similarity measure is employed to accurately estimate the conditional probability. Following this, we propose four potential approaches for combining loss functions by introducing the BULI ordered weighted averaging operator, which incorporates the consideration of decision risk. The derivation of the three-way decision rules is based on the comparison of BULI and the application of operational laws. In addition, the resolution of various issues such as the determination of criteria weights, identification of ideal solutions, and conversion of ratings to BULI is achieved through the development of a multi-criteria keyword frequency statistical method and a user credibility model. Subsequently, the algorithm for the dynamic three-way multi-criteria decision-making method, aimed at resolving product ranking issues, is presented. Eventually, we elaborate on the utilization of the proposed methodology through the illustration of a case study involving the ranking of passenger cars.

MCClusteringSM: An approach for the multicriteria clustering problem based on a credibility similarity measure

Multicriteria clustering problem has been studied and applied scarcely. When a multicriteria clustering problem is tackled with an outranking approach, it is necessary to include preferences of decision makers on the raw dataset, e.g., weights and thresholds of the evaluation criteria. Then, it is necessary to conduct a process to obtain a comprehensive model of preferences represented in a fuzzy or crisp outranking relation. Subsequently, the model can be exploited to derive a multicriteria clustering. This work presents an exhaustive search approach using a credibility similarity measure to exploit a fuzzy outranking relation to derive a multicriteria clustering. The work includes two experimental designs to evaluate the performance of the algorithm. Results show that the proposed method has good performance exploiting fuzzy outranking relations to create the clusterings.

A new ELECTRE-based decision-making framework with spherical fuzzy information for the implementation of autonomous vehicles project in Istanbul

This research study keenly employs the ground-breaking theory of ELECTRE method in order to advance the literature with a more competent technique for the frequently implemented and incredibly practical model of spherical fuzzy sets. The eminent characteristics, broader approach and adaptable model of spherical fuzzy set empowers the proposed strategy to captivate each type of the imprecise human judgement (whether in favor, against, neutral or hesitant) using a dependent and three independent membership grades. The remarkable novelty of the proposed approach is its successful application even with no criteria weights knowledge. Furthermore, the proposed technique processes the spherical fuzzy data and examines different types of preferences among alternatives using three threshold values. These preferences contribute to classify the outranking relations among the alternatives within five different levels of dominance. Later on, the iterative approach of distillation is deployed to figure out the final ranking. The proposed strategy is applied to meet the challenges in the implementation of autonomous vehicles in Turkey. After a through evaluation of five corridors via proposed technique in reference to fifteen criteria, metrobus corridor appears as the most appropriate for the autonomous vehicle operation. In the last, the consistency of the proposed approach is delineated via comparative analysis with existing spherical fuzzy CODAS and spherical fuzzy PROMETHEE approaches and other convincing evidences.

An application of generalized symmetric type-2 intuitionistic fuzzy variables to a transportation problem with the effect of a new ranking function

The primary objective of this study is to establish and introduce generalized symmetric type-2 intuitionistic fuzzy set, a type-2 fuzzy set extension. The major goal of such an extension is to enable the concept of fuzzy outranking relation which is known as foot print of uncertainty (FOU) to be employed in generalized symmetric type-2 intuitionistic fuzzy number (GST2IFN) for the first time. This is interesting to deal this GST2IFNs and it is complicated to find the equivalent crisp quantities due to this special structure. Hence, we have introduced a new ranking function using the mean level set of membership and non-membership functions. Besides, the arithmetic operations of the GST2IFNs are discussed. To express the effectiveness and application of the proposed GST2IF variables, a transportation problem (TP) is formulated with all cost parameters are GST2IFNs. In addition, few different algorithmic approaches are applied to find the initial basic feasible solution (IBFS) and optimal solution of the TP. Finally, the feasibility and richness of the obtained optimal solution is discussed.

Outranking-based failure mode and effects analysis considering interactions between risk factors and its application to food cold chain management

Failure mode and effects analysis (FMEA) is helpful to control potential risks of enterprises by ranking failure modes based on their evaluation values regarding multiple risk factors. However, due to the cognitive limitation of experts, obtaining explicit evaluation of failure modes is difficult; in addition, existing literature on FMEA rarely considered interactions between risk factors. To bridge these research gaps, this paper proposes an outranking-based hesitant fuzzy linguistic FMEA method. Firstly, inspired by the PROMETHEE-II and the best-worst method, the outranking relation between each failure mode and the best or the worst failure mode is determined, so as to reduce the number of pairwise comparisons. Also, the hesitant fuzzy linguistic term set is used to express the ambiguity of expert judgments regarding the comparisons of failure modes. The Choquet integral is applied to integrate the scores of risk factors considering their interactions. Finally, the proposed method is implemented in an example of food cold chain risk assessment. Results shows that failure modes with high risks are the illness of cold chain workers, product loss and long-time transportation. This study contributes theories about managing ranking efficiency of failure modes in FMEA under fuzzy evaluation environment with interactive risk factors when addressing food cold chain risk evaluation problems.

Regret-Theoretic Multiattribute Decision-Making Model Using Three-Way Framework in Multiscale Information Systems.

The existing multiattribute decision-making (MADM) methods on multiscale information systems (MSISs) are generally studied from the utility point of view, which may cause two problems: 1) the objects are strictly classified into good or bad, which may lead to misclassification and 2) the risk attitude and psychological behaviors of decision makers are difficult to be reflected. In light of this, this article proposes a wide three-way decision (3WD) model on an MSIS, which combines 3WD theory and regret theory and can precisely make up for these two shortcomings. First, by virtue of regret theory, an outranking relation on the comprehensive MSIS is constructed according to the regret-rejoicing index. Second, objects in the outranking relation are classified into three different domains by a clustering method. In each domain, the ranking of objects can be calculated by using the relative closeness coefficient. Finally, we use the cases in the database to simulate the experiment to verify the decision-making effect of the proposed model. Comparative analysis and experimental analysis also show the effectiveness, superiority, and stability of the proposed model.

Valued outranking relation-based heterogeneous multi-decision multigranulation probabilistic rough set and its use in medical decision-making

Regarding to group decision-making, decision makers may express different decision preferences for the same target. Since existing decision-making methods only consider a single decision preference at most, it is difficult to integrate the multi-decision preferences of multiple decision makers effectively. In this paper, we explore a class of heterogeneous multi-attribute group decision-making (MAGDM) problems with multiple decisions. Over the framework of granular computing, we present a new MAGDM method with the aid of the valued outranking relations in the ELECTRE III method. Considering the non-compensability among attributes, we first define a special valued outranking relation on a heterogeneous attribute multi-decision information system, and then present the notion of valued outranking classes. Subsequently, given the fact that the fault tolerance of probability approximations can reduce the vulnerability of decision-making methods, we construct multi-decision multigranulation probabilistic rough sets (MD-MGPRSs) form different perspectives, which extend existing rough set models to a more general situation. After that, we establish a novel MAGDM method, and apply the method to a real medical case to illustrate its applicability. Lastly, the experimental results on seven datasets demonstrate the effectiveness, robustness, and superiority of the method. Our study not only enriches the theoretical research of rough sets, but also extends the application scope of decision-making theory.

An outranking approach with 2-tuple linguistic Fermatean fuzzy sets for multi-attribute group decision-making

The 2-tuple linguistic Fermatean fuzzy set is an effective tool that combines the advantages of the reliable 2-tuple linguistic model with Fermatean fuzzy set. We aim to develop novel decision-making techniques based on 2TLFFS that can handle the situations in which linguistic labels are assigned to given data. The main objective of this study is to investigate ELECTRE II method for group decision-making in 2-tuple linguistic Fermatean fuzzy context and explain its implementation. The first phase employs suitable 2TLFF aggregation operator to assemble the expert’s 2TLFF judgments on each alternative and set of criteria. The method then introduces three different sets (2TLFF concordance, 2TLFF indifferent and 2TLFF discordance sets) by pairwise comparison of alternatives. After that, the strong and weak outranking relations are developed through the comparison of concordance and discordance indices with threshold values (three concordance and two discordance levels). The strong and weak outranking graphs visually represent outranking relations that are ultimately investigated through a systematic iterative process that results in the alternatives’ forward, reverse, and average rankings. A flowchart is developed to comprehend the algorithm of 2TLFF-ELECTRE II conveniently. A numerical example for the selection of optimal Extract, Transform and Load software for business intelligence describes the proposed decision-making technique. A thorough comparison with 2TLFF-CODAS and existing aggregation operators is carried out to illustrate the validity and supremacy of the proposed technique.

Integrated outranking techniques based on spherical fuzzy information for the digitalization of transportation system

The digitalization of the traditional technologies have facilitated humans globally as these technologies are not only time saving, modern, environment friendly but are economic as well. This study is concerned with the digitalization of the public transportation system in Istanbul to reduce the environmental pollution that is a big cause of different harmful diseases and climate changes. This article aims to manifest the decision-making aptitude of the elimination and choice translating reality methods working within the boundaries of modern, well structured and highly adaptable model of spherical fuzzy sets keenly focusing on group decision-making by presenting two significant techniques. Thus, the proposed methodologies operate by considering the dominance and subordination between alternatives relying on the prime concepts of concordance, discordance and indifferent sets. Further, these sets are exploited to determine the concordance and discordance indices in accordance with the weights of the decision criteria. Finally, the decision is made by observing the outranking relations among alternatives using the threshold values. The proposed strategies seek help from the technique of spherical fuzzy analytical hierarchy process for the evaluation of criteria weights. Noticing the widespread scope and appealing benefits of digital systems, this article targets the digitalization of the public transportation system in Istanbul. The potential of proposed approaches are highlighted via comparative studies.

Cubic bipolar fuzzy VIKOR and ELECTRE-II algorithms for efficient freight transportation in Industry 4.0

<abstract><p>The theory of cubic bipolar fuzzy sets (CBFSs) is a robust approach for dealing with vagueness and bipolarity in real-life circumstances. This theory provides a hybrid machine learning paradigm that can accurately describe two-sided contrasting features for medical diagnosis. The ELECTRE-II model, which is extensively used, is expanded in this article to include the cubic bipolar fuzzy (CBF) context. In order to produce a comprehensive preference ordering of actions, ELECTRE-II establishes two different forms of embedded outranking relations while taking into account the subjective human judgments. A huge number of applications have been created by its variations under various models, considering the CBF model's greater capacity to deal. For opinions in the adaptive CBF structure with unknown information, the CBF-ELECTRE-II group decision support method is described. With the use of proper CBF aggregation operations, the expert CBF views on each alternative and criterion are compiled in the first step. The approach then constructs weak and strong outranking relations and offers three distinct CBF outranking set kinds ("concordance", "indifferent" and "discordance" sets). Strong and weak outranking graphs serve as a visual depiction of the latter, which is finally studied by a rigorous iterative procedure that yields a preferred system. For these objectives, integrated CBF-VIKOR and CBF-ELECTRE-II techniques are developed for multi-criteria group decision making (MCDGM). Finally, suggested techniques are recommended to determine ranking index of efficient road freight transportation (FRT) in Industry 4.0. The ranking index and optimal decision are also computed with other techniques to demonstrate robustness of proposed MCDGM approach.</p></abstract>

Circular Intuitionistic Fuzzy ELECTRE III Model for Group Decision Analysis

ELECTRE III is a well-established outranking relation model used to address the ranking of alternatives in multi-criteria and multi-actor decision-making problems. It has been extensively studied across various scientific fields. Due to the complexity of decision-making under uncertainty, some higher-order fuzzy sets have been proposed to effectively model this issue. Circular Intuitionistic Fuzzy Set (CIFS) is one such set recently introduced to handle uncertain IF values. In CIFS, each element of the set is characterized by a circular area with a radius, r and membership/non-membership degrees as the centre. This paper introduces CIF-ELECTRE III, an extension of ELECTRE III within the CIFS framework, for group decision analysis. To achieve this, we define extensions for the group decision matrix and group weighting vector based on CIFS conditions, particularly focusing on optimistic and pessimistic attitudes. These attitudinal characters of the group of actors are constructed using conditional rules to ensure that each element of the set falls within the circular area. Parameterized by $\alpha \in [0,1]$ for the net score degree, we conduct an extensive analysis of group decision-making between optimistic and pessimistic attitudes. To illustrate the applicability of the proposed model, we provide a numerical example of the stock-picking process. Additionally, we conduct a comparative analysis with existing sets and perform sensitivity analyses to validate the results of the proposed model.

ELECTRE III for Human Resource Management: A Study of Recruitment and Retention Strategies

This paper presents the application of the ELECTRE III multi-criteria decision making method for human resource management. The case study conducted involves the evaluation of different recruitment and retention strategies using multiple criteria such as cost, time to fill a position, quality of candidates, retention rate, diversity and inclusion, employee satisfaction, and compliance. The study demonstrates the step-by-step process of how to conduct an ELECTRE III analysis, including the identification of criteria and alternatives, the calculation of concordance and discordance indices, and the determination of final rankings using the global outranking relation. The results of this analysis can be used by organizations to make informed decisions about recruitment and retention strategies that best align with their goals and objectives. The study highlights the importance of data quality and the need for sensitivity analysis to check the robustness of the results. Additionally, it is suggested that future research could be conducted on how to effectively communicate the results of the analysis to stakeholders and decision-makers, and on comparing the results of this analysis with other methods.

Improved Multidimensional Quality of Life Index Based on Outranking Relations

The aim of this research is to propose an improved multidimensional quality of life index, which could replace the current methodology designed by Eurostat and applied by the national statistical institutes of the European Union member states. The novelty of the proposed index is that it is based on a non-compensatory multicriteria decision method (ELECTRE III). All other quality of life indices propose compensatory aggregation methods at some stage in the construction of the index. The data used in this study are openly available on the website of the INE, which is the Spanish National Statistics Institute, and were obtained by INE from population surveys. The data were entered by the authors in the Diviz software to conduct an ELECTRE III method. Three innovative versions for the multidimensional quality of life index are proposed in this study, which are called Basic ELECTRE, Full ELECTRE, and Full Fuzzy ELECTRE. The comparison of the results obtained by INE with the results provided by our proposals shows that it is possible to construct an improved multidimensional quality of life index to be applied by the member states of the European Union.

A new hierarchical multiple criteria ordered clustering approach as a complementary tool for sorting and ranking problems

Multiple criteria ordered clustering is a problem that involves grouping the objects of decisions (actions) into a priori unknown ordered classes considering the preferences of a decision maker (DM). By exploring the relationship between multiple criteria sorting and multiple criteria ordered clustering, we take advantage of some ordinal classification approaches to propose a new approach. The set of totally ordered clusters fulfills a property of monotonicity with respect to dominance and an asymmetric preference relation; this is useful for suggesting a more consistent and robust rank of actions regarding the existence of possible “irrelevant alternatives”. The algorithm designed to operationalize our approach makes use of either a fuzzy outranking relation or a fuzzy preference relation. Imperfect knowledge (namely uncertainty and imprecision) of criteria performance levels and model parameter values can be modelled using interval numbers. Our approach and algorithm are illustrated through a simple interval extension of the well-known PROMETHEE method, which is applied to group countries according to human development criteria. The OECD country governments are also grouped according to their public sector performance but instead using a fuzzy outranking relation in an interval framework. In both examples, the results are very promising.

Penerapan Metode Promethee pada Sistem Pendukung Keputusan Penerimaan Beasiswa

A decision support system is a computer-based system that helps users in the decision-making process. In this study, the authors use the promethee method in the decision support system for receiving scholarships at the Information Systems Study Program, University of Flores. The Flores University Foundation Scholarship is a form of appreciation for the Foundation in helping potential students in the form of finances given in the current semester. This research was conducted by building outranking relations through pairwise comparisons by looking for the values ​​of entering flow, leaving flow and net flow. The stages in the promethee method consist of two main parts, namely the stages of preparation and data analysis. The purpose of this study is to apply the promethee method and build a website-based scholarship acceptance decision support system. This decision support system is designed using the PHP programming language and MySQL database. The type of research used is applied research, while the author's research method uses quantitative methods. The application of this decision support system is expected to facilitate the Study Program in the best decision-making process on receiving scholarships at the Fores University Information Systems Study Program.

Z-number dominance, support and opposition relations for multi-criteria decision-making

The outranking multi-criteria decision-making (MCDM) method focuses on the systematic comparison of alternatives and the non-compensation among criteria to conduct decision analysis. Z-numbers are powerful tools for characterizing decision-making information and identifying information reliability. This paper aims to explore Z-number outranking theories and the corresponding MCDM method on the basis of the idea of Elimination and Choice Translating Reality (ELECTRE) III, which is a very popular and capable outranking model. The concordance and discordance indices of Z-numbers are defined by processing their bimodal uncertainty fully. And an objective computation method is investigated to determine the values of thresholds in these indices. Further, three types of novel outranking relations, including dominance, support and opposition relations, for Z-numbers are presented by comparing these indices systematically. Then, an extended MCDM method with the distillation and flow ranking rules is proposed by detecting the outranking relations among alternatives under multiple criteria and carrying out the outranking aggregation and exploitation. To verify the applicability and effectivity of the proposed method, an illustrative example is provided, and a simulation test and a comparison discussion are conducted

A stochastic method for exploiting outranking relations in multicriteria choice problems

A stochastic method for exploiting outranking relations in multicriteria choice problems