Optimizing Multimodal Transportation: A Novel Decision-Making Approach With Fuzzy Risk Assessment - R. Muthunandhini; K. Palanivel (IEEE Access)
In today’s complex transportation networks, optimizing multimodal transportation decisions is critical for enhancing efficiency and sustainability. On improving multimodal transportation, there is still a complexity, ambiguity and risk occur during route selection from one place to another. To handle such scenarios efficiently, this study proposes a novel approach that integrates Fuzzy Risk Assessment with three advanced multi-criteria decision-making (MCDM) methods: the Best-Worst Method (BWM), Superiority-Inferiority Ranking (SIR) method, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The BWM is employed to determine the precise weights of the criteria, ensuring that decision-making reflects the relative importance of each factor. The SIR method is utilized to rank the alternatives based on their superiority and inferiority, providing a clear hierarchy of options. Finally, TOPSIS is used as an aggregate operator in the SIR method to synthesize the results, offering a comprehensive ranking that accounts for the proximity of each alternative to an ideal solution. The integration of these methods within a fuzzy risk assessment framework allows for a robust handling of uncertainties and subjective judgments that are inherent in multimodal transportation planning. The proposed approach is tested on a case study involving the optimization of a multimodal transportation network, demonstrating its effectiveness in providing a clear, rational, and reliable ranking of transportation alternatives. The results underscore the potential of this hybrid methodology to enhance decision-making in complex, real-world transportation scenarios. The advantage of using dual-faceted model incorporates diverse transportation methods and fuzzy logic for risk evaluation, thus enabling balanced, adaptive decision-making. This system can analyze scenarios that can help select optimal transportation modes with economic and environmental difficulties.