A Hybrid AI–Fuzzy–OR Model Simulation for Multi-Objective Optimization in Sustainable Transportation Networks

Abstract

Cost and time are the main dimensions for a successful businesses. Supply chain require high accuracy systems ensuring sustainable transport reducing cost, time and CO2 emissions. Traditionally,  operational research (OR) techniques are implemented for problem solving and optimization purposes. However, transport has a dynamic work environment that require to employ an intelligent approaches to overcome data uncertainty. Despite fuzzy logic multi-objective utilized to enhance the supply chains performance, defuzzification methods still in place for multi-modal logistics. Thus, hybridising multi-technique including AI-enhanced, fuzzy logic, and OR will potentially provide an optimized approach to reduce cost, time and decrease carbon-intensive emissions for transport in supply chain. The results have been reduced relatively to the baseline allocations by employing LP, the cost decreased by (2.5%), time (1.9%), and emissions (3.0%).