Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neural Computing and Applications
Published in: Neural Computing and Applications 7/2019

25-10-2017 | Original Article

Group decision making with interval fuzzy preference relations based on DEA and stochastic simulation

Authors: Jinpei Liu, Qin Xu, Huayou Chen, Ligang Zhou, Jiaming Zhu, Zhifu Tao

Published in: Neural Computing and Applications | Issue 7/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper proposes an integrated approach to group decision making with interval fuzzy preference relations using data envelopment analysis (DEA) and stochastic simulation. A novel output-oriented CCR DEA model is proposed to obtain the priority vector for the consistency fuzzy preference relation, in which each of the alternatives is viewed as a decision-making unit. Meanwhile, we design a consistency adjustment algorithm for the inconsistent fuzzy preference relation. Furthermore, we build an optimization model to get the weights of each fuzzy preference relation based on maximizing group consensus. Then, an input-oriented DEA model is introduced to obtain the final priority vector of the alternatives. Finally, a stochastic group preference analysis method is developed by analyzing the judgments space, which is carried out by Monte Carlo simulation. A numerical example demonstrates that the proposed method is effective.
previous article Figure-ground image segmentation using feature-based multi-objective genetic programming techniques
next article Query-specific optimal convolutional neural ranker

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Aguarón J, Moreno-Jiménez JM (2003) The geometric consistency index: approximated thresholds. Eur J Oper Res 147:137–145MATHCrossRef
2.
Bentkowska U, Bustince H, Jurio A, Pagola M, Pekala B (2015) Decision making with an interval-valued fuzzy preference relation and admissible orders. Appl Soft Comput 35:792–801CrossRef
3.
4.
Chen H, Zhou L (2011) An approach to group decision making with interval fuzzy preference relations based on induced generalized continuous ordered weighted averaging operator. Expert Syst Appl 38(10):13432–13440CrossRef
5.
6.
Fernandez E, Leyva JC (2004) A method based on multiobjective optimization for deriving a ranking from a fuzzy preference relation. Eur J Oper Res 154:110–124MathSciNetMATHCrossRef
7.
Gong Z, Xu X, Zhang H, Ozturk UA, Herrera-Viedma E, Xu C (2015) The consensus models with interval preference opinions and their economic interpretation. Omega 55:81–90CrossRef
8.
Herrera-Viedma E, Herrera F, Chiclana F, Luque M (2004) Some issues on consistency of fuzzy preference relations. Eur J Oper Res 154(1):98–109MathSciNetMATHCrossRef
9.
10.
Liao H, Xu Z (2014) Priorities of intuitionistic fuzzy preference relation based on multiplicative consistency. IEEE Trans Fuzzy Syst 22(6):1669–1681CrossRef
11.
Ma J, Fan ZP, Jiang YP, Mao JY, Ma L (2006) A method for repairing the inconsistency of fuzzy preference relations. Fuzzy Sets Syst 157(1):20–33MathSciNetMATHCrossRef
12.
13.
Pérez-Fernández R, Alonso P, Bustince H, Díaz I, Montes S (2016) Applications of finite interval-valued hesitant fuzzy preference relations in group decision making. Inf Sci 326:89–101MathSciNetMATHCrossRef
14.
Pula W (1985) On the accuracy of calculation of probability by simulation method. In: Proc. Conf. Mathematical Method in Technology Poland
15.
16.
Wang YM, Fan ZP, Hua Z (2007) A chi square method for obtaining a priority vector from multiplicative and fuzzy preference relations. Eur J Oper Res 182(1):356–366MATHCrossRef
17.
Wang YM, Parkan C (2005) Multiple attribute decision making based on fuzzy preference information on alternatives: ranking and weighting. Fuzzy Sets Syst 153:331–346MathSciNetMATHCrossRef
18.
Wu DD (2009) Performance evaluation: an integrated method using data envelopment analysis and fuzzy preference relations. Eur J Oper Res 194(1):227–235MathSciNetMATHCrossRef
19.
Wu J, Chiclana F (2014) A social network analysis trust–consensus based approach to group decision-making problems with interval-valued fuzzy reciprocal preference relations. Knowl-Based Syst 59(2):97–107CrossRef
20.
Xia M, Xu Z, Chen J (2013) Algorithms for improving consistency or consensus of reciprocal [0,1]-valued preference relations. Fuzzy Sets Syst 216:108–133MathSciNetMATHCrossRef
21.
Xia M, Chen J (2015) Studies on interval multiplicative preference relations and their application to group decision making. Group Decis Negot 24(1):1–30CrossRef
22.
Xu ZS, Chen J (2008) Some models for deriving the priority weights from interval fuzzy preference relations. Eur J Oper Res 184:266–280MathSciNetMATHCrossRef
23.
Xu ZS (2002) Research on compatibility and consistency of fuzzy complementary judgment matrices. J PLA Univ Sci Technol 3(2):94–96
24.
25.
Xu Z, Cai X (2010) Uncertain power average operators for aggregating interval fuzzy preference relations. Group Decis Negot 21(3):381–397CrossRef
26.
Xu ZS, Da QL (2005) A least deviation method to obtain a priority vector of a fuzzy preference relation. Eur J Oper Res 164:206–216MATHCrossRef
27.
Zhang H (2016) Group decision making based on multiplicative consistent reciprocal preference relations. Fuzzy Sets Syst 282(1):31–46MathSciNetMATHCrossRef
28.
Zhou LG, He YD, Chen HY, Liu JP (2014) Compatibility of interval fuzzy preference relations with the COWA operator and its application to group decision making. Soft Comput 18:2283–2295MATHCrossRef
29.
Zhu B, Xu Z (2014) A fuzzy linear programming method for group decision making with additive reciprocal fuzzy preference relations. Fuzzy Sets Syst 246(4):19–33MathSciNetMATHCrossRef
30.
31.
Zhu B, Xu Z, Xu J (2013) Deriving a ranking from hesitant fuzzy preference relations under group decision making. IEEE Trans Cybern 44(8):1328–1337CrossRef
Metadata
Title
Group decision making with interval fuzzy preference relations based on DEA and stochastic simulation
Authors
Jinpei Liu
Qin Xu
Huayou Chen
Ligang Zhou
Jiaming Zhu
Zhifu Tao
Publication date
25-10-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 7/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3254-7

Other articles of this Issue 7/2019

Neural Computing and Applications 7/2019 Go to the issue

Original Article

Quantum multiverse optimization algorithm for optimization problems

Original Article

Nonparametric kernel smoother on topology learning neural networks for incremental and ensemble regression

Original Article

New predictive method for estimation of natural gas hydrate formation temperature using genetic programming

Original Article

A generalized reinforcement learning scheme for random neural networks

Original Article

Neural networks ensemble for automatic DNA microarray spot classification

Original Article

Statistical machine translation of Indian languages: a survey

Premium Partner

    Image Credits