A Hybrid ANP-TOPSIS Method for Strategic Supplier Selection in RL under Rough Uncertainty: A Case Study in the Electronics Industry
Keywords:
RL , Strategic supplier selection, Strategic alliance , Multi-criteria decision-making , Rough WASPAS, ANP, TOPSIS, Rough set theory, Electronics industryAbstract
The efficient management of Reverse Logistics (RL) is essential for organizations aiming to achieve sustainability goals and gain competitive advantage. This study addresses the complexities of RL, particularly within the electronics industry, by proposing a hybrid decision-making framework that integrates the Analytic Network Process (ANP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) within a rough uncertainty environment. Traditional supplier selection methods often overlook intricate interdependencies between criteria and struggle with uncertain information. The hybrid ANP-TOPSIS method combines the strengths of both approaches, offering a comprehensive evaluation of strategic alliance suppliers. Key criteria for supplier selection, including knowledge management, risk sharing, and quality, are identified and applied in a case study within the electronics industry. The results demonstrate the robustness and reliability of the proposed framework in ranking suppliers and provide valuable insights for enhancing RL operations. This research contributes to advancing Multi-Criteria Decision-Making (MCDM) methodologies. It offers practical recommendations for companies facing similar logistical challenges, bridging the gap between academic theory and real-world application in RL management.
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