Multigroup and multiple criteria decision analysis methods: models based on information processing

• Grant number: 16/21571-4
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: March 01, 2017
• End date: November 30, 2020
• Field of knowledge: Engineering - Electrical Engineering
• Principal Investigator: João Marcos Travassos Romano
• Grantee: Guilherme Dean Pelegrina
• Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated scholarship(s): 17/23879-9 - Choquet integrals in multi-group multi-criteria decision making, BE.EP.DR

Abstract

Decision making problems involving multiple criteria can be found in several practical situations. Many of these problems have specific characteristics that can lead to biased results, especially with respect to the size of the problem data. For example, when there is a large number of criteria, many of them may not be necessary in terms of expressiveness in the model, or even be correlated, introducing redundancy in the evaluation of the alternatives. These characteristics become more evident when we consider the involvement of a group of agents in decision-making. In this multiple criteria multigroup formulation, besides the large size with respect to multiple criteria, the number of data is multiplied by the number of agents, leading to a more complex treatment of the opinions considered in the problem. However, additional considerations of agents may be incorporated into the model, as the experience of each one in decision-making or the influence of one over another in consensus reaching. In this context, one may note a need for searching models that deal with multigroup and multiple criteria decision problems that take into account the information processing contained in the data and additional characteristics that may be incorporated into the model. In order to handle the information in the problem and mitigate possible subjectivities incorporated into the resolution model, in this research project, we aim at developing and investigating information processing methods that deal with the data set of multigroup and multiple criteria decision problems. Moreover, from the proposed models, we also aim at offering a solution to the multigroup decision problem with a satisfactory consensus with respect to the agents

Scientific publications

(5)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

PELEGRINA, GUILHERME DEAN; DUARTE, LEONARDO TOMAZELI; GRABISCH, MICHEL; TRAVASSOS ROMANO, JOAO MARCOS; TORRA, V; NARUKAWA, Y; NIN, J; AGELL, N. An Unsupervised Capacity Identification Approach Based on Sobol' Indices. MODELING DECISIONS FOR ARTIFICIAL INTELLIGENCE (MDAI 2020), v. 12256, p. 12-pg., 2020-01-01. (17/23879-9, 16/21571-4)

PELEGRINA, GUILHERME DEAN; DUARTE, LEONARDO TOMAZELI; GRABISCH, MICHEL; TRAVASSOS ROMANO, JOAO MARCOS. The multilinear model in multicriteria decision making: The case of 2-additive capacities and contributions to parameter identification. European Journal of Operational Research, v. 282, n. 3, p. 945-956, MAY 1 2020. (16/21571-4, 17/23879-9)

PELEGRINA, GUILHERME DEAN; DUARTE, LEONARDO TOMAZELI; TRAVASSOS ROMANO, JOAO MARCOS. Application of independent component analysis and TOPSIS to deal with dependent criteria in multicriteria decision problems. EXPERT SYSTEMS WITH APPLICATIONS, v. 122, p. 262-280, MAY 15 2019. (17/23879-9, 16/21571-4)

PELEGRINA, GUILHERME DEAN; DUARTE, LEONARDO TOMAZELI; GRABISCH, MICHEL; ROMANO, JOAO MARCOS TRAVASSOS. Dealing with redundancies among criteria in multicriteria decision making through independent component analysis. COMPUTERS & INDUSTRIAL ENGINEERING, v. 169, p. 19-pg., 2022-04-26. (20/01089-9, 17/23879-9, 20/09838-0, 16/21571-4)

PELEGRINA, GUILHERME DEAN; DUARTE, LEONARDO TOMAZELI; TRAVASSOS ROMANO, JOAO MARCOS; DEVILLE, Y; GANNOT, S; MASON, R; PLUMBLEY, MD; WARD, D. Muticriteria Decision Making Based on Independent Component Analysis: A Preliminary Investigation Considering the TOPSIS Approach. LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION (LVA/ICA 2018), v. 10891, p. 10-pg., 2018-01-01. (16/21571-4, 17/23879-9)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

PELEGRINA, Guilherme Dean. Tomada de decisão multicritério: lidando com interações entre critérios por meio da análise de variáveis latentes. 2020. Doctoral Thesis - Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação Campinas, SP.