运筹与管理 ›› 2021, Vol. 30 ›› Issue (6): 42-47.DOI: 10.12005/orms.2021.0178

• 理论分析与方法探讨 • 上一篇    下一篇

基于多目标属性权重优化的犹豫模糊语言TOPSIS决策方法

吴澎1,2, 吴群1, 周礼刚1, 陈华友1   

  1. 1.安徽大学 数学科学学院,安徽 合肥 230601;
    2.安徽大学 商学院,安徽 合肥 230601
  • 收稿日期:2019-07-02 出版日期:2021-06-25
  • 作者简介:吴澎(1990-),男,安徽宿州人,讲师,博士,研究方向:预测和决策;吴群(1994-),男,安徽枞阳人,硕士研究生,研究方向:预测和决策;周礼刚(1980-),男,安徽潜山人,教授,博士生导师,研究方向:预测和决策;陈华友(1969-),男,安徽和县人,教授,博士生导师,研究方向:预测和决策。
  • 基金资助:
    国家自然科学基金资助项目(71771001,71871001);安徽省哲学社会科学规划项目(AHSKQ2020D10);安徽省自然科学基金杰出青年基金(1908085J03);安徽省学术和技术带头人及后备人选资助项目(2018H179);全国统计科学研究项目(2019LY66)

Hesitant Fuzzy Linguistic TOPSIS Decision Making Method Based on Multi-objective Attribute Weight Optimization

WU Peng1,2, WU Qun1, ZHOU Li-gang1, CHEN Hua-you1   

  1. 1. School of Mathematical Sciences, Anhui University, Hefei 230601, China;
    2. School of Business, Anhui University, Hefei 230601, China
  • Received:2019-07-02 Online:2021-06-25

摘要: 犹豫模糊语言术语集作为一种有效的信息表达形式,能够很好的反映出人们的定性且犹豫的决策信息。传统的距离测度会导致犹豫模糊语言信息的流失,因此,本文首先提出了一种新的犹豫模糊语言距离测度,并研究了该距离测度的性质。其次,针对属性权重完全未知的犹豫模糊语言多属性决策问题,考虑方案和属性两个层面,构建了多目标优化的属性权重确定模型。进而,基于多目标权重优化模型和犹豫模糊语言距离测度,提出了一种改进的犹豫模糊语言TOPSIS法。最后通过实例说明了所提出的TOPSIS法的实用性和有效性,并进行了灵敏度和比较分析。

关键词: 多属性决策, 犹豫模糊语言术语集, 改进TOPSIS, 多目标优化

Abstract: Hesitant fuzzy linguistic term set (HFLTS)as an useful information expression form can well reflect qualitative and hesitant decision-making information of people. Traditional distance measures may cause loss of information of hesitant fuzzy linguistic. Therefore, this paper first proposes a new distance measure between HFLTSs and discusses some relevant properties. Then, for hesitant fuzzy linguistic multi-attribute decision-making with unknow attribute weights, aiming at the two aspects of alternative and attribute, the multi-objective optimization attribute weight determination model is proposed to obtain the weights of attributes. Furthermore, an improved TOPSIS method is developed based on the multi-objective weight optimization model and the new distance measure. In the end, a practical example is provided to verify the practicability and effectiveness of the proposed method, sensitive and comparative analysis are provided as well.

Key words: multi-attribute decision-making, hesitant fuzzy linguistic term set, improved TOPSIS, multi-objective optimization

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