Fuzzy 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.

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.

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.

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.

A Personnel Selection Model for a Software Development Company based on the ELECTRE III Method and a Variant of NSGA-II

Personnel selection represents a valuable decision-making process that determines, in some way, the competitiveness and performance of an organization. The essential elements of the personnel selection task are the position requirements and the accessible information related to candidates. In this paper, the personnel selection task is modeled as a multicriteria decision-making problem, considering varied competencies and skills to assess the applicants for a specific position. We used this multicriteria model to solve personnel selection cases using sadgage, a decision support system that solves examples of the multicriteria ranking problem in decreasing order based on the preferences of the decision-maker. The electre-iii method is embedded in the software in order to construct a fuzzy outranking relation and a multi-objective evolutionary algorithm to exploit such relation and generate a ranking as a recommendation. This work presents a practical application to a personnel selection problem that evaluates a group of applicants to a software developer job in an enterprise based in northwestern Mexico. We show how the proposed multicriteria procedure offers a recommendation to the decision-maker about the applicants in decreasing order of preferences.

Non-dominated sorting genetic-based algorithm for exploiting a large-sized fuzzy outranking relation

Electre III is a well-known multiple criteria decision aiding method based on pairwise comparisons. However, it cannot be applied to ranking problems involving many alternatives, because the number of pairwise comparisons can then be rather large. In this paper, we present an evolution-based approach for exploiting large fuzzy outranking relations and deriving a crisp outranking relation with desirable properties. Therefore, the utilization of a fuzzy outranking relation is modeled as a three-objective optimization problem, which is solved by an evolutionary algorithm. The proposed ranking algorithm is a hybrid of the elitist non-dominated sorting genetic algorithm-II (NSGA-II) and a reference point method with the repeated use of a choice mechanism. In addition, a method that portrays the obtained ranking in a Hasse diagram is used for recommendation purposes. We designate the new method RP2-NSGA-II+H. In our experiments, the proposed ranking procedure demonstrates a better performance in terms of ranking error rates than other ranking procedures based on multi-objective evolutionary algorithms. Our experimental results also demonstrate that, with the new procedure, this method can be scaled for hundreds of alternatives.

A multiobjective evolutionary algorithm for deriving a final ranking from a medium-sized fuzzy outranking relation

A multiobjective evolutionary algorithm for deriving a final ranking from a medium-sized fuzzy outranking relation

Development of Conversational Deliberative Agents Driven by Personality via Fuzzy Outranking Relations

Currently, there are many types of conversational agents whose goal is to emulate human behavior. These agents offer more believable conversations when their responses come from a deliberative process that mimics individuals’ character. Conversational agents are mainly used for response selection linguistics and context situated strategies. These approaches usually build rules to find answers in dialogues; however, this is not the best alternative when the communicative intentions are not literal and context dependent. Deliberative Agents can solve these issues and improve their selection process through the integration of preferences and personality in their cognitive process. Hence, this work investigates how to drive the expression of dialogues of a Conversational Deliberative Agent (CDA) through personality and fuzzy outranking relations; for this, it proposes the characterization of context, and corpus through speech acts theory, and also a selection process based on fuzzy outranking relations to compare corpus phrases and context to choose the best response. The main contributions of this work are (1) the agent architecture that integrates preferences and personality of an individual in the response selection cognitive process; (2) a characterization model of speech acting through criteria based on belief, desires, and intentions to define a more human behavior expression; and (3) the use of fuzzy outranking relations to select phrases from a corpus to match dialogue intentions. An experimental design demonstrated the aptitude of the developed CDA to offer quality responses on a tutor application. Also, the results showed the capacity of speech acts to handle contexts in dialogues.

Parallel Designs for Metaheuristics that Solve Portfolio Selection Problems Using Fuzzy Outranking Relations

In decision-making, the multiobjective portfolio selection problem (MPSP) consists of the selection of alternatives based on preferences of a particular decision-maker (DM). In real-world applications, MPSP has several conflicting criteria that DMs must consider to determine an appropriate solution. So far, only fuzzy outranking relations have been used in a relational system of preferences to guide the search process of genetic algorithms (NOSGAII), and ant colony optimization (NOACO) to approximate the region of interest (RoI) of MPSP involving DM’s preferences. The NOSGAII and NOACO strategies are sequential, and they face a real challenge when solving high-dimensional instances which is a decrement in the computational efficiency due to the increment in the number of objectives. The present research proposes to use parallelism to tackle the efficiency situation in metaheuristics. The study first identifies which approach approximates better the RoI, and then, it analyzes the effect of parallelism in the performance. The results showed that NOACO found more best compromises in almost all the instances than NOSGAII. Hence, it can be concluded that NOACO approximates better the RoI. Also, the results showed a better average speedup with coarse-grained parallelism in NOACO than with data-flow parallelism, suggesting the conclusion that ants independently working are faster than ants working collaboratively. Finally, the main contributions are (1) the analysis of the performance of the two approaches for MPSP, (2) the five parallel designs for NOACO, and (3) the parallel NOACO that speedups up to 2× the sequential version when solving MPSP.

A multiobjective evolutionary algorithm for deriving a final ranking from a medium-sized fuzzy outranking relation

The exploitation stage of a fuzzy outranking relation of the outranking methods aims to deal with the intransitivities in such a relation to consolidate and synthesise the decision maker's preferences between pairs of actions to obtain a ranking that is representative of his/her preferences over a given set of actions. In this paper, we propose this exploitation phase as a multiobjective optimisation problem and use a modified version of the multiobjective genetic algorithm (we refer to it as RP2-MOGA+H) to exploit a medium-sized fuzzy outranking relation to determining a partial pre-order of alternatives. To measure the performance of RP2-MOGA+H, we present an empirical study over a set of simulated ranking problems, which shows that RP2-MOGA+H can effectively exploit a medium-sized fuzzy outranking relation. Moreover, the RP2-MOGA+H outperforms other ranking procedures based on multiobjective evolutionary algorithms in the conducted experiments.

A metaheuristic optimization-based indirect elicitation of preference parameters for solving many-objective problems

A priori incorporation of the decision maker’s preferences is a crucial issue in many-objective evolutionary optimization. Some approaches characterize the best compromise solution of this problem through fuzzy outranking relations; however, they require the elicitation of a large number of parameters (weights and different thresholds). This paper proposes a novel metaheuristic-based optimization method to infer the model’s parameters of a fuzzy relational system of preferences, based on a small number of judgments given by the decision maker. The results show a satisfactory rate of error when predicting new outcomes with the parameter values obtained by using small size reference sets.

A web-based group decision support system for multicriteria ranking problems

In this paper, we present an implemented, web-based multicriteria group decision support system for solving multicriteria ranking problems by a collaborative group of decision-makers in sequential or parallel coordination mode and in a distributed and asynchronous environment. This system employs an order-based consensus model for collaborative groups that moves from consistency to consensus. The system is based on consensus measures and it has been designed to provide advice to the decision-makers to increase group consensus level while maintaining the individual consistency of each decision-maker. It is based on the use of fuzzy outranking relations to model individual and group preferences. For the exploitation of the models—formulated as a multiobjective optimization problem and solved with a multiobjective evolutionary algorithm—the system generates advice on how decision-makers should change their preferences to reach a ranking of alternatives with a high degree of consistency and consensus.

Exploitation of a Medium-Sized Fuzzy Outranking Relation Based on Multi-objective Evolutionary Algorithms to Derive a Ranking

AbstractWe present a multi-objective evolutionary algorithm to exploit a medium-sized fuzzy outranking relation to derive a partial order of classes of alternatives (we call it RP2-NSGA-II). To measure the performance of RP2-NSGA-II, we present an empirical study over a set of simulated multi-criteria ranking problems. The result of this study shows that RP2-NSGA-II can effectively exploit a medium-sized fuzzy outranking relation. Finally, we present a real-case study for ranking the municipalities of the state of Guanajuato, Mexico by their levels of marginalization.

An outranking sorting method for multi-criteria group decision making using intuitionistic fuzzy sets

In real world multi-criteria group decision making (MCGDM) problems, the decision making information provided by the decision makers is often imprecise or uncertain because of a lack of data, time pressure, or the decision makers’ limited information-processing capabilities. Intuitionistic fuzzy sets, however, have been shown to have greater imprecision and an increased ambiguity than ordinary fuzzy sets. For these reasons, this paper proposes a new outranking sorting method for group decision making using intuitionistic fuzzy sets. Based on a proposed intuitionistic fuzzy support function, risk function and credibility function, we first propose a new method for the construction of an intuitionistic fuzzy outranking relation to exploit the sorting problems. Then we extend the proposed intuitionistic fuzzy sorting method to take account of group decision techniques and to develop an adaptive search and adjustment approach for the group consensus. Finally, a numerical example for a bridge risk assessment is provided to elucidate the details of the proposed method, and then this method is compared with other current methods to further demonstrate its flexibility.

An automatic ranking approach for multi-criteria group decision making under intuitionistic fuzzy environment

Multi-criteria group decision making (MCGDM) problems are complex issues often faced by decision makers. In the decision making process, to deal with fuzziness and hesitation, the decision criteria provided by decision makers may take the form of intuitionistic fuzzy numbers. In this paper, we propose an automatic ranking approach for MCGDM under an intuitionistic fuzzy environment. We first propose a new method for the construction of an intuitionistic fuzzy outranking relation to exploit a ranking problem. Then we extend the intuitionistic fuzzy ELECTRE III method to take into account group decision techniques and develop an automatic approach to achieving group opinion satisfaction, which can avoid forcing the decision makers to modify their decision matrices. Finally, a numerical example for supplier evaluation is provided to elucidate the details of the proposed method.

Application of the non-outranked sorting genetic algorithm to public project portfolio selection

This paper proposes the application of multi-criteria analysis to the problem of allocating public funds to competing programs, projects, or policies, with a subjective approach applied to define the concept of highest portfolio social return. This portfolio corresponds to an attainable non-strictly outranked state of the social object under consideration. Its existence requires a decision-maker (DM) to establish a relational preference system of minimal consistency. As the number of feasible portfolios increases exponentially, the DM’s asymmetric preference relation should be computable to perform an exploration of the portfolio space. The complexity of many real situations requires evolutionary algorithms, but, in presence of many objectives, evolutionary algorithms are inefficient. We overcome this problem by using the extended non-outranked sorting genetic algorithm (NOSGA-II), which handles multi-criteria preferences through a robust model based on a binary fuzzy outranking relation expressing the truth value of the predicate “portfolio x is at least as good as portfolio y”. The DM is assumed to be capable of assigning the parameters for constructing the outranking relation. In case of collective decision-making, we first assume that the possible differing values of the group members are not strongly conflicting, so that a consensus can be achieved on the model’s parameters. Otherwise, we propose a method in which each member of the heterogeneous group gets his/her own best portfolio. These individual solutions are then aggregated in a group’s best acceptable portfolio, which maximizes a measure of group satisfaction and minimizes regret.The proposal is examined through two real size problems, in which good solutions are reached; the first example is useful to illustrate the case of social-action program selection; the second illustrates the case of basic research project portfolios.

An outranking-based general approach to solving group multi-objective optimization problems

This paper presents a general approach to solving multi-objective programming problems with multiple decision makers. The proposal is based on optimizing a bi-objective measure of “collective satisfaction”. Group satisfaction is understood as a reasonable balance between the strengths of an agreeing and an opposing coalition, considering also the number of decision makers not belonging to any of these coalitions. Accepting the vagueness of “collective satisfaction”, even the vagueness of “person satisfaction”, fuzzy outranking relations and other fuzzy logic models are used.Our method transforms a group multi-objective optimization problem into a group choice problem on a decision set composed of a relatively small set of alternatives. This set contains the possible acceptable consensuses in the parameter space. Once such a set has been identified, other well-known techniques can be used to reach the final choice.Main advantages: (a) Each individual decision maker is concerned with his/her own multi-objective optimization problem, only sharing decision variables; own constraints and own mapping between decision variables and objective space are allowed; (b) the search for the best agreement is not limited to portions of the Pareto frontiers; (c) no voting rule is used by the optimization algorithm; no to some extent arbitrary way of handling collective preferences is needed; (d) no assumptions of transitivity and comparability of preference relations are needed; and (e) the concepts of satisfaction/non-satisfaction do not depend on distance measures or other to some extent arbitrary norms.Very good performance of the whole proposal is illustrated by a real-size example.

Evolutionary multi-objective optimization for inferring outranking model’s parameters under scarce reference information and effects of reinforced preference

AbstractMethods based on fuzzy outranking relations constitute one of the main approaches to multiple criteria decision problems. The use of ELECTRE methods require the elicitation of a large number of parameters (weights and different thresholds); but direct eliciting is often a demanding task for the decision-maker (DM). For handling intensity-ofpreference effects on concordance levels, a generalized concordance model was proposed by Roy and Slowinski which is more complex than previous outranking models. In this paper, an evolutionary multi-objective-based indirect elicitation of the complete ELECTRE III model-parameter set is proposed. The evolutionary multi-objective inference method is successfully extended to inferring reinforced-preference model parameters. Wide experimental evidence is provided to support the proposal, which performs well even working on small size reference sets.

A new approach to multi-criteria sorting based on fuzzy outranking relations: The THESEUS method

In this paper, we propose the THESEUS method, a new approach based on fuzzy outranking relations to multi-criteria sorting problems. Compared with other outranking-based methods, THESEUS is inspired by another view of multi-criteria classification problems. It utilizes a new way of evaluating the assignment of an object to an element of the set of ordered categories that were previously defined. This way is based on comparing every possible assignment with the information from various preference relations that are derived from a fuzzy outranking relation defined on the universe of objects. The appropriate assignment is determined by solving a simple selection problem. The capacity of a reference set for making appropriate assignments is related to a good characterization of the categories. A single reference action characterizing a category may be insufficient to achieve well-determined assignments. In this paper, the reference set capacity to perform appropriate assignments is characterized by some new concepts. This capacity may be increased when more objects are added to the reference set. THESEUS is a method for handling the preference information contained in such larger reference sets.

DETERMINING THE CAUSES OF DEMOCRATIC CONSOLIDATION: A CONSIDERATION OF SEVERAL FUZZY METHODS

We use techniques of group decision making and consensus under fuzzy preferences and fuzzy majority developed so as to compare five methods used for determining the degree of causality of eight variables for democratic consolidation. In particular for Q denoting the linguistic variable 'most', we determine the fuzzy Q-core, i.e. the fuzzy subset of the set of causal variables that are not defeated (are undominated or unpreferred) by the methods. We also calculate the Q-consensus winner, i.e. the fuzzy subset of causal variables yielding the extent to which one variable is preferred to another, where 'preferred' here means stronger cause for democratic consolidation. Finally, we use the techniques of fuzzy outranking relations in preference modeling to generate relational systems of fuzzy preferences by the five methods used to determine the degree of causality of the causal variables.