Roberta Parreiras

A flexible consensus scheme for multicriteria group decision making under linguistic assessments

Abstract The present paper proposes a flexible consensus scheme for group decision making, which allows one to obtain a consistent collective opinion, from information provided by each expert in terms of multigranular fuzzy estimates. It is based on a linguistic hierarchical model with multigranular sets of linguistic terms, and the choice of the most suitable set is a prerogative of each expert. From the human viewpoint, using such model is advantageous, since it permits each expert to utilize linguistic terms that reflect more adequately the level of uncertainty intrinsic to his evaluation. From the operational viewpoint, the advantage of using such model lies in the fact that it allows one to express the linguistic information in a unique domain, without losses of information, during the discussion process. The proposed consensus scheme supposes that the moderator can interfere in the discussion process in different ways. The intervention can be a request to any expert to update his opinion or can be the adjustment of the weight of each expert’s opinion. An optimal adjustment can be achieved through the execution of an optimization procedure that searches for the weights that maximize a corresponding soft consensus index. In order to demonstrate the usefulness of the presented consensus scheme, a technique for multicriteria analysis, based on fuzzy preference relation modeling, is utilized for solving a hypothetical enterprise strategy planning problem, generated with the use of the Balanced Scorecard methodology.

Fuzzy Multicriteria Decision-Making: Models, Methods and Applications

Fuzzy Multicriteria Decision-Making: Models, Algorithms and Applications addresses theoretical and practical gaps in considering uncertainty and multicriteria factors encountered in the design, planning, and control of complex systems. Including all prerequisite knowledge and augmenting some parts with a step-by-step explanation of more advanced concepts, the authors provide a systematic and comprehensive presentation of the concepts, design methodology, and detailed algorithms. These are supported by many numeric illustrations and a number of application scenarios to motivate the reader and make some abstract concepts more tangible.

A dynamic consensus scheme based on a nonreciprocal fuzzy preference relation modeling

In this paper, we propose a dynamical consensus scheme which makes use of an index of comparability and of an index of concordance to regulate the information flow in the discussion among the experts. In addition to the traditional use of a concordance index to identify the discordant expert, who can be invited to review his opinion or to explain his judgment to the other experts, here an index of comparability is utilized to identify the experts that have faced difficulties in expressing their preferences. With this information at hand, a moderator can invite the experts that are confident on their opinions to support the experts that are hesitating in the construction of the fuzzy preference relations.

On multicriteria decision making under conditions of uncertainty

This paper deals with multicriteria decision making problems under conditions of uncertainty. The main its contribution is the consideration of choice criteria of the classic approach to handle information uncertainty in monocriteria decision making as objective functions within the framework of multiobjective models, whose analysis generates harmonious solutions. Such consideration of choice criteria is of a fundamental character and allows one to modify the generalization, originally proposed by Ekel, Martini, and Palhares (2008), of the classic approach to handle information uncertainty for solving multicriteria problems. The modification permits one to overcome limitations of the indicated generalization, which can lead to contradictory decisions. Details of using the modification in a general scheme of multicriteria decision making under uncertainty are presented. The general scheme is focused on the use of available quantitative information to the highest degree to reduce decision uncertainty regions. However, if the problem solving capacity concerning quantitative information processing does not allow one to obtain unique solutions, the general scheme assumes the use of qualitative information based on knowledge, experience, and intuition of experts involved in the decision making process. Examples are presented to illustrate the modification of generalizing the classic approach as well as the general scheme of multicriteria decision making under information uncertainty.

A Web-based Decision Support Center for Electrical Energy Companies

This study describes a framework for a Web-based decision support center (DSC) to aid various interrelated decisionmaking situations, which emerge from planning and management processes in electrical energy companies. This framework supports small collaborative groups working in an asynchronous way, in an environment where a single decision-maker (DM), who centralizes the responsibility for a final decision, can be aided by a group of experts, who contribute with their opinions to that decision. It is based on the construction and the analysis of (X, R) models, where X is a set of feasible solutions and R a set of fuzzy preference relations. Two preference formats, namely value functions and fuzzy sets, are made available to the input of preference information. Transformation functions adequate for dealing with preference measures on interval scales are utilized to construct fuzzy preference relations. The (X, R) models are analyzed by means of procedures based on the use of the Orlovsky choice function. The availability of different aggregation operators allows a DM to reproduce different attitudes: pessimistic, optimistic, compensatory with adjustment of the tradeoff rates among criteria, as well as lexicographic with prioritization of criteria. When the DM cannot choose a unique attitude to analyze a problem, DSC recommends a generalized solution, which considers all attitudes simultaneously. To demonstrate the applicability of the framework, an expansion planning decision-making problem is considered.

Construction of nonreciprocal fuzzy preference relations with the use of preference functions

In order to model the preferences of a decision-maker (DM) by means of fuzzy preferencerelations, a DM can utilize different preference formats (such as ordering of the alternatives, utility values, multiplicative preference relations, fuzzy estimates, and reciprocal as well as nonreciprocal fuzzy preference relations) to express his/her judgments. Afterward, the obtained information is utilized to constructfuzzy preference relations. Here we introduce a procedure that allows the use of so-called preference functions (which is a preference format utilized in the methods of PROMETHEE family) to construct nonreciprocal fuzzy preference relations. With diverse preference formats being offered, a DM can select the onethat is the most convenient to articulate his/her preferences. In order to demonstrate the applicability of theproposed procedure a multicriteria decision-making problem related to the site selection for constructing anew hospital is considered here.

Fuzzy Preference Based Organizational Performance Measurement

This paper introduces a methodology for constructing a multidimesional indicator designed for organizational performance measurement. The methodology involves the application of fuzzy models and methods of their analysis. Its use requires the construction of fuzzy preference relations by means of the comparison of performance measures with respect to a reference standard defined as a predetermined scale consisting of linguistic terms. The exploitation of the fuzzy preference relations is carried out by means of the Orlovsky choice procedure. An application example related to the organizational performance evaluation with the use of the proposed methodology is considered, in order to demonstrate its applicability.

A flexible multicriteria decision-making methodology to support the strategic management of Science, Technology and Innovation research funding programs

Abstract Research funding programs are a policy instrument utilized by governments to influence the innovation process. They are usually elaborated, launched and managed by research funding agencies. In order to select the most adequate research projects, agencies often rely on the peer review process. This paper introduces a methodology to support funding decisions based on the peer review process. The methodology involves the use of a multicriteria decision model to support the assessment, evaluation, prioritization and selection of applications, under a multi-step decision-making process, which fits into a strategic management cycle within the agency. The Multiattribute Value Theory, being considered under a Value Focused Thinking approach, provides a basis for the construction of the multicriteria decision model. The good practices in peer review and also a logical framework for program management are considered by the methodology. A pilot study, presented in the paper, involved a retrospective implementation of a peer review process in the context of a program launched by the Ministry for Science, Technology, Innovations and Communications and the National Council of Technological and Scientific Development, in Brazil. The methodology allowed a clear distinction of roles. The agency staff in the role of decision-makers was responsible for making value judgments on behalf of the agency. The experts, in the role of committee members and ad hoc reviewers, contributed with their expertise by providing objective assessments. Such assessments served as a basis for evaluating the applications, characterizing the possible portfolios, and can be considered as data in future program evaluation studies.