Scholarlink

<p>In this paper, we propose a new distance measure between spherical fuzzy sets (SFSs) to overcome the drawbacks of existing distance measures between SFSs. We develop a modified symmetry point of criterion (SPC) weight-determining method to get attributes’ weights based on developed distance measure between SFSs and the spherical fuzzy weighted aggregation operators. In the conventional SPC method, a symmetry point is estimated as the middle value or the average value of a given interval [<i>a</i>, <i>b</i>], where <i>a</i> and <i>b</i> denote the maximum rating and the minimum rating of a criterion, respectively, and then it employed the absolute distance measure to find the modulus of symmetry (MOS) matrix of attributes, but the SPC method is unable to describe the imprecise and the uncertain data during the determining of the weights of attributes. We further propose a new multi-attribute decision-making (MADM) method based on the proposed modified SPC weight-determining method and the compromise ranking of alternatives from the distance to the ideal solution (CRADIS) approach using the proposed distance measure between SFSs. The proposed MADM approach can overcome the drawbacks of the existing MADM approaches in the SFSs setting. It offers us a very useful approach for MADM in the context of SFSs. To illustrate the applicability of proposed framework, it is applied to a case study of industry 4.0 enabling technologies (I4.0ETs) to achieve the digital transformation in the photovoltaic sustainable supply chain (PVSSC). The outcomes demonstrate that “Artificial intelligence” is the most suitable I4.0ETs to achieve digital transformation in the PVSSC among the considered six I4.0ETs.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-attribute Decision-Making Based on New Distance Measure Between Spherical Fuzzy Sets, Modified Symmetry Point of Criterion Weight-Determining Method, and CRADIS Approach

  • 期刊论文

  • 发表日期：

    2026年1月8日

查看全文
  • Pratibha Rani,
  • Shyi-Ming Chen,
  • Arunodaya Raj Mishra

摘要

In this paper, we propose a new distance measure between spherical fuzzy sets (SFSs) to overcome the drawbacks of existing distance measures between SFSs. We develop a modified symmetry point of criterion (SPC) weight-determining method to get attributes’ weights based on developed distance measure between SFSs and the spherical fuzzy weighted aggregation operators. In the conventional SPC method, a symmetry point is estimated as the middle value or the average value of a given interval [a, b], where a and b denote the maximum rating and the minimum rating of a criterion, respectively, and then it employed the absolute distance measure to find the modulus of symmetry (MOS) matrix of attributes, but the SPC method is unable to describe the imprecise and the uncertain data during the determining of the weights of attributes. We further propose a new multi-attribute decision-making (MADM) method based on the proposed modified SPC weight-determining method and the compromise ranking of alternatives from the distance to the ideal solution (CRADIS) approach using the proposed distance measure between SFSs. The proposed MADM approach can overcome the drawbacks of the existing MADM approaches in the SFSs setting. It offers us a very useful approach for MADM in the context of SFSs. To illustrate the applicability of proposed framework, it is applied to a case study of industry 4.0 enabling technologies (I4.0ETs) to achieve the digital transformation in the photovoltaic sustainable supply chain (PVSSC). The outcomes demonstrate that “Artificial intelligence” is the most suitable I4.0ETs to achieve digital transformation in the PVSSC among the considered six I4.0ETs.