A rankability-based fuzzy decision making procedure for oil supplier selection

Multiple-criteria decision-making (MCDM) explicitly assesses several conflicting criteria for our daily lives in selecting products, vehicles, techniques, etc. Weighting on criteria is a critical step in MCDM as the invalid weight of criteria will lead to a wrong decision. The proposed method addresses some drawbacks of the entropy-based weighting method commonly used in MCDM. The proposed new weighting method considers multiple evaluation factors, including the performance of the decision-maker, the edge weight basis of a digraph, and dominance relationships in the data. By incorporating these factors, the proposed method overcomes the limitations of the entropy-based method and reduces the total computation required. We conducted experiments using sustainable transportation data and comprehensively analyzed the results. We also propose a fuzzy MCDM model incorporating the proposed weighting method and Dempster-Shafer theory. Our model aims to handle uncertainty and imprecision in decision-making. Finally, the correctness and effectiveness of the proposed model were tested on real-life applications. The results of these tests demonstrated that the proposed method provides a practical and effective approach to decision-making in various domains. Overall, the work introduces a new weighting method based on rankability in MCDM, addresses the limitations of the entropy-based method, and presents a fuzzy MCDM model for handling uncertainty. The experimental results suggest that the proposed approach is promising and offers valuable insights for decision-makers.

Automated summary

This study proposes a new weighting method for multiple-criteria decision-making (MCDM) that addresses the limitations of the commonly used entropy-based method. By considering multiple evaluation factors and incorporating them into the weighting method, the proposed approach overcomes the limitations of the entropy-based method and reduces computation requirements. The study also introduces a fuzzy MCDM model for handling uncertainty and tests the correctness and effectiveness of the proposed approach on real-life applications. The experimental results suggest that the proposed approach is promising and offers valuable insights for decision-makers.