ELECTRE TRI-C with Hesitant Fuzzy Sets and Interval Type 2 Trapezoidal Fuzzy Numbers Using Stochastic Parameters: Application to a Brazilian Electrical Power Company Problem

被引:0
|
作者
Pereira, Javier [1 ]
de Oliveira, Elaine C. B. [2 ]
Morais, Danielle C. [3 ]
Costa, Ana Paula C. S. [3 ]
Alencar, Luciana H. [3 ]
机构
[1] Univ Tecnol Chile Inacap, Santiago, Chile
[2] Inst Fed Paraiba, Campus Joao Pessoa, Joao Pessoa, Brazil
[3] Univ Fed Pernambuco UFPE, Ctr Decis Syst & Informat Dev CDSID, Recife, Brazil
关键词
Group decision; ELECTRE TRI-C; Hesitant fuzzy sets; Interval type 2 trapezoidal fuzzy number; Robustness analysis; MULTICRITERIA DECISION-MAKING; ACCEPTABILITY ANALYSIS; OUTRANKING APPROACH; SORTING METHOD; SYSTEM; MODEL; EXTENSION; SELECTION; LOCATION; SUPPORT;
D O I
10.1007/s40815-024-01775-3
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
ELECTRE TRI-C is a method for sorting problems with imprecise evaluations and stable criteria weights, typically for a single decision-maker. While some extensions have addressed uncertain criteria weights and outranking functions using hesitant fuzzy sets (HFS) and interval type 2 trapezoidal fuzzy numbers (IT2TrfN), there is a gap in handling situations where multiple decision-makers provide uncertain information. This paper presents an extension of the ELECTRE TRI-C method incorporating a stochastic framework to model HFS and IT2TrfN, thereby accommodating subjective judgments from multiple decision-makers. The extended method was validated by sorting 49 projects based on their criticality in a Brazilian electrical power company, involving three decision-makers. The application shows strong correlations in project rankings among decision-makers, but with some exceptions. However, significant variations in acceptability ratings for sorting among decision-makers lead to notable error dispersion, highlighting differences between ranking and sorting outcomes. The key contributions of our approach are as follows: (1) Integration of subjective judgments from multiple decision-makers using IT2TrFN and Monte Carlo Simulation for constructing outranking functions; (2) Aggregation of preferences from multiple decision-makers using HFS; (3) Stochastic processing of both quantitative and qualitative criteria; (4) Integration of linear equations to represent weight constraints; and (5) Introduction of a novel visualization method for comprehensive analysis of stochastic results, enhancing robustness analysis. The proposal's advantages over alternative methods are also highlighted.
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页数:17
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