Proportional Pythagorean Fuzzy AHP & TOPSIS Methodology for the Evaluation of Industry 5.0 Projects


ÇEBİ S., Uçal Sarı İ., Kahraman C., Öztayşi B., Çevik Onar S., Tolga C.

Journal of Multiple-Valued Logic and Soft Computing, cilt.43, sa.4-6, ss.523-548, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 43 Sayı: 4-6
  • Basım Tarihi: 2024
  • Dergi Adı: Journal of Multiple-Valued Logic and Soft Computing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, zbMATH
  • Sayfa Sayıları: ss.523-548
  • Anahtar Kelimeler: AHP, Industry 5.0, Proportional fuzzy sets, Pythagorean Fuzzy Sets, TOPSIS
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

The term, Industry 5.0 refers to a transformation in production systems with the key priorities: human-machine collaboration, sustainability, and social responsibility. The focus of Industry 5.0 on intelligent collaboration between human operators and machines brings a potential for efficiency and innovation for companies. In order to realize these potential benefits, organizations need to initiate new projects and make new investments. From the organization's perspective, this brings a project prioritization and selection problem. The project selection problem refers to the evaluation of potential projects according to various criteria and make a decision on initiating the project. By its nature, the problem is a multi-criteria decision-making problem since it involves various criteria. In this study, a decision model involving six criteria, namely compatibility, scalability, reliability, security, cost-effectiveness, and human-machine collaboration is developed. A hybrid decision-making model that integrates Proportional Pythagorean Fuzzy AHP & TOPSIS is proposed to solve the Industry 5.0 selection problem. The sensitivity analysis, comparative analysis, and discussions with decision-makers reveal that the proposed approach provides a robust, and easy-to-use methodology.