Interval-valued intuitionistic hesitant fuzzy entropy based VIKOR method for industrial robots selection

被引:138
作者
Narayanamoorthy, S. [1 ]
Geetha, S. [1 ]
Rakkiyappan, R. [1 ]
Joo, Young Hoon [2 ]
机构
[1] Bharathiar Univ, Dept Math, Coimbatore, Tamil Nadu, India
[2] Kunsan Natl Univ, Dept IT Informat Control Engn, Kunsan 573701, Chonbuk, South Korea
基金
新加坡国家研究基金会;
关键词
Intuitionistic fuzzy set; Hesitant fuzzy set; Interval-valued hesitant fuzzy set; Fuzzy entropy; Industrial robots; DECISION-MAKING; INFORMATION MEASURES; SETS; SYSTEM;
D O I
10.1016/j.eswa.2018.12.015
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we proposed interval valued intuitionistic hesitant fuzzy entropy for determine the importance of the criteria and interval valued intuitionistic hesitant fuzzy VIKOR method for ranking the alternatives. Industrial Robots are utilized to perform complicated and hazardous tasks accurately and also used to enhance the quality and efficiency of the work. Selecting an industrial robot for performing a particular task depends on the work and the associated criteria of the robot. The materials handled by the robots are different like powdered, adhesive, bulky, brittle etc. In this manner, choosing a suitable robot from the set of available industrial robots to handle a particular material is a challenging task. To get a more conscionable decision result, a decision organization contains a lot of decision makers. The interval- valued intuitionistic hesitant fuzzy set is utilized as a competent mathematical tool for enunciate individuals hesitant thinking. An interval- valued intuitionistic hesitant fuzzy set (IVIHFS) concedes a set of several possible interval- valued intuitionistic fuzzy membership and non- membership values. Finally, the proposed interval- valued intuitionistic hesitant fuzzy entropy and VIKOR techniques utilized for industrial robot selection. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:28 / 37
页数:10
相关论文
共 40 条
  • [1] Necessary and possible hesitant fuzzy sets: A novel model for group decision making
    Alcantud, Jose Carlos R.
    Giarlotta, Alfio
    [J]. INFORMATION FUSION, 2019, 46 : 63 - 76
  • [2] INTERVAL VALUED INTUITIONISTIC FUZZY-SETS
    ATANASSOV, K
    GARGOV, G
    [J]. FUZZY SETS AND SYSTEMS, 1989, 31 (03) : 343 - 349
  • [3] INTUITIONISTIC FUZZY-SETS
    ATANASSOV, KT
    [J]. FUZZY SETS AND SYSTEMS, 1986, 20 (01) : 87 - 96
  • [4] Selection of industrial robots using compromise ranking method
    Athawale, Vijay Manikrao
    Chatterjee, Prasenjit
    Chakraborty, Shankar
    [J]. International Journal of Industrial and Systems Engineering, 2012, 11 (1-2) : 3 - 15
  • [5] A De Novo multi-approaches multi-criteria decision making technique with an application in performance evaluation of material handling device
    Bairagi, Bipradas
    Dey, Balaram
    Sarkar, Bijan
    Sanyal, Subir Kumar
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2015, 87 : 267 - 282
  • [6] Selection of robot for automated foundry operations using fuzzy multi-criteria decision making approaches
    Bairagi, Bipradas
    Dey, Balaram
    Sarkar, Bijan
    Sanyal, Subir
    [J]. INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT, 2014, 9 (03) : 221 - 232
  • [7] A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method
    Boran, Fatih Emre
    Genc, Serkan
    Kurt, Mustafa
    Akay, Diyar
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (08) : 11363 - 11368
  • [8] Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets
    Burillo, P
    Bustince, H
    [J]. FUZZY SETS AND SYSTEMS, 1996, 78 (03) : 305 - 316
  • [9] Selection of industrial robots using compromise ranking and outranking methods
    Chatterjee, Prasenjit
    Athawale, Vijay Manikrao
    Chakraborty, Shankar
    [J]. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2010, 26 (05) : 483 - 489
  • [10] Interval-valued hesitant preference relations and their applications to group decision making
    Chen, Na
    Xu, Zeshui
    Xia, Meimei
    [J]. KNOWLEDGE-BASED SYSTEMS, 2013, 37 : 528 - 540