Developing Industry 4.0-Based E-Waste Management: A Decision-Aided Tool Equipped With Continuous Function-Valued Intuitionistic Fuzzy Sets

Aydoğan B., Özçelik G., Ünver M.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, sa.(Accepted), 2024 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s13762-024-05977-y
  • Dergi Adı: INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, CAB Abstracts, Compendex, Environment Index, Geobase, INSPEC, Pollution Abstracts, Veterinary Science Database
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Electronic waste (e-waste) is a global problem with an increasing environmental impact every day. The impact on the environment, on the lives of living beings, and on the pollution and destruction of nature is escalating day by day. Given the scale of the problem, there is an urgent need to identify and implement solutions. The strategies to be developed should be innovative and aligned with today’s technological advancements, including artifcial intelligence. To this end, the aim of this study is to present an original fuzzy decision-aided framework for ensuring sustainable e-waste management within the context of key Industry 4.0 strategies. This study uses original data and is supported by expert opinion. Additionally, the advantages of continuous function-valued intuitionistic fuzzy sets (CFVIFSs), an innovative approach, are utilized. The criteria weighting is enhanced by the Kullback–Leibler divergence measure formed with these CFVIFSs, adding another dimension to the study. Results are achieved using Goal Programming (GP) approach in strategy selection. In addition, detailed and comparative analyses are conducted to evaluate strategy rankings from diferent perspectives. In the sensitivity analysis, the rankings are obtained according to the varying weights of criteria. Furthermore, The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and The Simple Additive Weighting (SAW) method are employed for the comparative analysis. Moreover, Spearman’s rank correlation coefcients are calculated to examine the consistency of each case. Overall, this study, which brings together diferent perspectives, provides valuable managerial insights to a global problem.