Scholarlink

<p>In a realistic scenario, particularly within complex systems such as engineering construction, conventional TOPSIS methodology (Technique for Order Preference by Similarity to Ideal Solution) often struggles to address uncertainties, bounded rationality, and the reverse order phenomenon. This paper develops a variant of the interval TOPSIS approach. Using comprehensive prospect value and cloud distance as the calculation basis, the approach captures the risk preferences of decision-makers, and mitigates reverse phenomena associated with Euclidean distance failure, providing a practical tool for engineering decisions. Through a case study that selects an optimal supplier of prefabricated components, the effectiveness of the proposed methodology is verified using simulation, validity, sensitivity, and comparative analyses. The decision outcome aligns with the actual choice and offers superior applicability, significance, robustness, and accuracy.</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

Selecting an optimal alternative: a multi-criteria decision-making approach with cloud distance and prospect theory

  • 期刊论文

  • 发表日期：

    2026年2月28日

查看全文
  • Zhang Xiang,
  • Huang Jianhua

摘要

In a realistic scenario, particularly within complex systems such as engineering construction, conventional TOPSIS methodology (Technique for Order Preference by Similarity to Ideal Solution) often struggles to address uncertainties, bounded rationality, and the reverse order phenomenon. This paper develops a variant of the interval TOPSIS approach. Using comprehensive prospect value and cloud distance as the calculation basis, the approach captures the risk preferences of decision-makers, and mitigates reverse phenomena associated with Euclidean distance failure, providing a practical tool for engineering decisions. Through a case study that selects an optimal supplier of prefabricated components, the effectiveness of the proposed methodology is verified using simulation, validity, sensitivity, and comparative analyses. The decision outcome aligns with the actual choice and offers superior applicability, significance, robustness, and accuracy.