Route selection in multimodal supply chains: A fuzzy risk assessment model-BWM-MARCOS framework

Abstract

Selecting a route in distribution networks can be considered as a multiple criteria decision-making problem owing to the involvement of conflicting decision criteria, many possible alternatives, different preferences, and uncertainties. Therefore, in this study, we proposed a new hybrid decision-making framework to select an optimal transportation route in multimodal supply chains. The proposed framework innovatively integrates the fuzzy risk assessment model (FRAM), best-worst method (BWM), and measurement of alternatives and ranking according to the compromise solution (MARCOS). FRAM-based fuzzy inference reasoning was applied to calculate the highly reliable risk magnitudes of the qualitative decision criteria. The BWM was used to determine the weights of all decision criteria to reduce the complexity and calculation procedures. MARCOS was employed to rank multimodal routes based on the effects of optimistic (ideal) and pessimistic (anti-ideal) solutions. The proposed framework was validated through an empirical study of route selection within the Greater Mekong sub-region to demonstrate its usefulness and applicability. The findings showed that the hybrid FRAM-BWM-MARCOS methodology employs the predominant features of each method to effectively optimize the route selection problem. It ranks all multimodal transportation routes and identifies the most appropriate alternative as the best compromise solution of the problem. The proposed framework can be viewed as an expert system that aids decision makers and stakeholders in developing new interactive freight distribution plans and transportation policies in fuzzy environments.