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Multi-attribute decision-making for electronic waste recycling using interval-valued Fermatean fuzzy Hamacher

Anam Luqman1, Gulfam Shahzadi2

  • 1Division of Science and Technology, Department of Mathematics, University of Education, Lahore, Pakistan.

Granular Computing
|April 16, 2024
PubMed
Summary

Selecting optimal waste electrical and electronics equipment (WEEE) recycling partners is crucial for environmental sustainability. This study introduces novel multi-attribute decision-making (MADM) techniques using interval-valued Fermatean fuzzy information to improve WEEE recycler selection.

Keywords:
Electronic waste recycling in ChinaHamacher AOsIVFFSsMADMWeighted averaging operatorsWeighted geometric operators

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Area of Science:

  • Environmental Science
  • Decision Science
  • Fuzzy Mathematics

Background:

  • Waste electrical and electronics equipment (WEEE) recycling is vital for environmental protection and reducing carbon emissions.
  • Selecting appropriate WEEE recycling partners is a complex decision-making (DM) challenge due to numerous qualitative and quantitative factors.
  • Effective WEEE recycling partner assessment is essential for sustainable environmental growth.

Purpose of the Study:

  • To propose novel multi-attribute decision-making (MADM) techniques for evaluating WEEE recycling partners.
  • To develop a decision-making method using interval-valued Fermatean fuzzy (IVFF) information and Hamacher aggregation operators (AOs).
  • To address the complex problem of selecting WEEE recycling partners in an eco-friendly manner.

Main Methods:

  • Development of specific Hamacher aggregation operators, including weighted averaging and geometric variants.
  • Application of these operators within an MADM framework under interval-valued Fermatean fuzzy (IVFF) conditions.
  • Implementation of the proposed technique in a case study for e-waste recycling partner selection in China.

Main Results:

  • The proposed MADM technique effectively evaluates WEEE recycling partners using IVFF information.
  • A case study in China demonstrated the practical application and efficacy of the developed method.
  • Comparison with existing techniques confirmed the proposed model's productivity, coherence, accuracy, and consistency.

Conclusions:

  • The developed MADM approach provides a robust framework for selecting WEEE recycling partners.
  • The technique is highly compatible and applicable for handling complex decision-making problems in WEEE recycling.
  • This research contributes to more eco-friendly and efficient WEEE recycling practices through improved partner selection.