Group multi-criteria design concept evaluation using combined rough set theory and fuzzy set theory

被引:96
|
作者
Shidpour, Hesam [1 ]
Da Cunha, Catherine [1 ]
Bernard, Alain [1 ]
机构
[1] LUNAM Univ, Ecole Cent Nantes, IRCCyN, CNRS,UMR 6597, 1 Rue Noe, F-44321 Nantes 3, France
关键词
Design concept evaluation; Rough set; Fuzzy set; Fuzzy AHP; Extent analysis method; Interval-based relative closeness index; EXTENT ANALYSIS METHOD; CONCEPT SELECTION; PRODUCT DESIGN; AHP; CLASSIFICATION; ALTERNATIVES; OPTIMIZATION; PERFORMANCE; MODEL; TOOL;
D O I
10.1016/j.eswa.2016.08.022
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Design concept evaluation is a critical stage in the product development which has significant impact on the downstream process in product development thus on success of new product. Design concept evaluation is widely recognized as a complex multi-criteria decision-making (MCDM) problem involving various decision criteria and large amount of data which are usually imprecise and subjective. This paper proposes a new decision-making method to evaluate product design concepts based on the distance between interval vectors each alternative and positive and negative ideal reference vectors. Rank of design concepts is obtained by calculating interval-based relative closeness index for each alternative. In this method, to deal with uncertainty and vagueness of data in the primary phases of product design, performance of design concepts with respect to quantitative and qualitative criteria are concurrently evaluated using rough set and fuzzy set. The weights of criteria used in the evaluation are obtained using the extent analysis method on fuzzy AHP. The efficacy of the method is demonstrated with a numerical example and the results are compared to TOPSIS method. In final, the conclusions of our method are represented and some future directions are proposed to improve the model. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:633 / 644
页数:12
相关论文
共 50 条
  • [31] Fuzzy Evaluation for Wireless Sensor Networks Based on Rough Set Theory
    Zhang, Lun
    Lu, Yan
    Chen, Lan
    2008 IEEE INTERNATIONAL PERFORMANCE, COMPUTING AND COMMUNICATIONS CONFERENCE (IPCCC 2008), 2008, : 406 - 411
  • [32] Rough set theory for group decision analysis
    An, LP
    PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING, VOLS 1 AND 2, 2004, : 158 - 162
  • [33] Concept lattices of fuzzy contexts: Formal concept analysis vs. rough set theory
    Lai, Hongliang
    Zhang, Dexue
    INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2009, 50 (05) : 695 - 707
  • [34] Multi-Criteria Group Decision-Making Method Using New Score Function Based on Vague Set Theory
    Lin, Kuo-Sui
    Chiu, Chih-Chung
    2017 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY), 2017,
  • [35] Fuzzy Set Theory. The Concept of Fuzzy Sets
    Medynskaya, M. K.
    2015 XVIII International Conference on Soft Computing and Measurements (SCM), 2015, : 30 - 31
  • [36] Using fuzzy set theory to support design representation
    Schwartz, DR
    Teng, WG
    10TH INTERNATIONAL CONFERENCE ON COMPUTER APPLICATIONS IN INDUSTRY AND ENGINEERING, 1997, : 13 - 16
  • [37] On the connection of hypergraph theory with formal concept analysis and rough set theory
    Cattaneo, Gianpiero
    Chiaselotti, Giampiero
    Ciucci, Davide
    Gentile, Tommaso
    INFORMATION SCIENCES, 2016, 330 : 342 - 357
  • [38] Early analysis of design concepts using rough set theory
    Alisantoso, D.
    Khoo, L. P.
    INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2009, 40 (02) : 121 - 130
  • [39] An approach to the analysis of design concepts using the rough set theory
    Alisantoso, D
    Khoo, LP
    Lee, IBH
    AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING, 2004, 18 (04): : 343 - 355
  • [40] Weighted support vector machine using fuzzy rough set theory
    Moslemnejad, Somaye
    Hamidzadeh, Javad
    SOFT COMPUTING, 2021, 25 (13) : 8461 - 8481