Document Type : Research Paper
Authors
Institute of Mathematics, University of the Punjab, New Campus, Lahore 4590, Pakistan
Abstract
Evaluating third-party reverse logistics providers (3PRLPs) is a complex task, often challenged by cognitive biases and
incomplete, uncertain data in group decision-making contexts. To overcome these challenges, this study proposes a
novel decision-support framework that integrates Fermatean fuzzy rough numbers with an extended entropy weight
method and a new ranking technique. Fermatean fuzzy rough numbers effectively capture uncertainty and subjectivity
without relying on predefined parameters, addressing key limitations in provider evaluation. The extended entropy
weight method objectively determines the importance of decision criteria across economic, environmental, social, and
risk dimensions. The framework enhances decision accuracy by integrating three ranking techniques into an aggregated
index, minimizing information loss while accommodating expert preferences. A case study on sustainable 3PRLP
evaluation in the Malaysian food industry demonstrates the model’s practicality, while sensitivity and comparative
analyses validate its robustness and superiority over existing approaches.
Keywords
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