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N-type-2-ARAS: An efficient hybrid multi-criteria optimization approach for end-of-life vehicle’s recycling facility location: A sustainable approach
Faculty
Computer Science
Year:
2024
Type of Publication:
ZU Hosted
Pages:
Authors:
Mohammed Abdel Basset Metwally Attia
Staff Zu Site
Abstract In Staff Site
Journal:
Expert Systems with Applications Elsevier
Volume:
Keywords :
N-type-2-ARAS: , efficient hybrid multi-criteria optimization approach
Abstract:
End-of-life vehicle (ELV) management is currently one of the most pressing environmental issues. Waste must be recycled to ensure that ELV management is both environmentally and economically sustainable. Cairo boasts the greatest percentage of Egyptian citizens who own a car and the country's oldest automotive fleet. There is a great desire to open a new ELV recycling factory in this city. One of the most difficult strategic issues facing decision-makers is determining where to build new facilities. ELV recycling facility selection is a multi-criteria and highly uncertain challenge, and this research presents a novel approach to help with the process. An extension of the Additive Ratio Assessment (ARAS) approach in a neutrosophic type-2 environment ((N-type-2 ARAS)) is described for the first time. The new approach (N-type-2 ARAS) is used to choose an ELV recycling facility location. The proposed N-type-2 ARAS method's potential and applicability are illustrated in the context of a real-life case study in Egypt. This study used five main criteria, twenty sub-criteria, and four alternatives (locations) to select the best location. The results show that Luxor is the best location. This method has been proven reliable and consistent compared to the current state-of-the-art interval type-2 fuzzy set-based MCDM methods. This study's sensitivity analysis was conducted to show rank stability from the (N-type-2 ARAS) methodology.
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Saber Mohamed, "Training and Testing a Self-Adaptive Multi-Operator Evolutionary Algorithm for Constrained Optimization", ELSEVEIR, 2015
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