Affiliation:
1. Islamic Azad University Semnan Branch
Abstract
Abstract
This study presents a novel hybrid approach for Multiple Attribute Decision-Making (MADM), integrating the Data Envelopment Analysis (DEA), Best Worst Method (BWM), and KEmeny Median Indicator Ranks Accordance (KEMIRA) methods. The proposed approach utilizes DEA to streamline the weight selection process in decision-making. By combining BWM and KEMIRA, the hybrid approach improves the accuracy and efficiency of attribute ranking and decision-making. Empirical results demonstrate the effectiveness of the proposed approach in addressing MADM problems with multiple attributes. The weighted analysis perspective provided by this hybrid approach offers valuable insights into decision-making processes, assisting decision makers in making informed choices. This research contributes to the advancement of MADM methodologies and introduces a new approach for handling complex decision scenarios.
Publisher
Research Square Platform LLC
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