Scholarlink

<p>Rapid urbanization and population growth have made solid waste management (SWM) a critical environmental challenge, particularly in developing regions. Inefficient systems significantly harm ecosystems, pose risks to public health, and impose substantial burdens on local economies. Therefore, artificial intelligence (AI) has garnered considerable attention as a potentially valuable tool for addressing the complexity of SWM. This study presents the complex Fermatean fuzzy sets (CFFS), a robust framework for addressing the vagueness and uncertainty inherent in decision-making. In this context, the key features of Yager aggregation operators (AOs), such as CFF Yager weighted average, CFF Yager weighted geometric, CFF Yager ordered weighted average, and CFF Yager ordered weighted geometric versions, are investigated. To increase the objectivity and reliability of multi-criteria evaluations, a complex multi-stage decision-making model combining the CRITIC and TOPSIS methods is embedded in the CFF environment. The CRITIC method objectively determines the relative importance of evaluation criteria, whereas TOPSIS facilitates ranking and selecting optimal alternatives. The model’s practical value is illustrated through a case study on AI-based SWM, supported by thorough sensitivity and comparison assessments. The outcomes demonstrate the effectiveness of the proposed approach and underscore its potential to foster cleaner, healthier, and more sustainable urban environments.</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

A Decision Support Model for Artificial Intelligence-Based Solid Waste Management Through CRITIC-TOPSIS and Complex Fermatean Fuzzy Structure

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月23日

查看全文
  • Umar Ali,
  • Junxiang Li,
  • Yasir Ahmad,
  • Fikadu Tesgera Tolasa,
  • Tapan Senapati

摘要

Rapid urbanization and population growth have made solid waste management (SWM) a critical environmental challenge, particularly in developing regions. Inefficient systems significantly harm ecosystems, pose risks to public health, and impose substantial burdens on local economies. Therefore, artificial intelligence (AI) has garnered considerable attention as a potentially valuable tool for addressing the complexity of SWM. This study presents the complex Fermatean fuzzy sets (CFFS), a robust framework for addressing the vagueness and uncertainty inherent in decision-making. In this context, the key features of Yager aggregation operators (AOs), such as CFF Yager weighted average, CFF Yager weighted geometric, CFF Yager ordered weighted average, and CFF Yager ordered weighted geometric versions, are investigated. To increase the objectivity and reliability of multi-criteria evaluations, a complex multi-stage decision-making model combining the CRITIC and TOPSIS methods is embedded in the CFF environment. The CRITIC method objectively determines the relative importance of evaluation criteria, whereas TOPSIS facilitates ranking and selecting optimal alternatives. The model’s practical value is illustrated through a case study on AI-based SWM, supported by thorough sensitivity and comparison assessments. The outcomes demonstrate the effectiveness of the proposed approach and underscore its potential to foster cleaner, healthier, and more sustainable urban environments.