Résumé

The increasing demand for efficient global supply chain management and faster product delivery has led to a rise in the use of multimodal transportation systems (MFT). One of the key challenges in multimodal transportation is selecting the appropriate freight mode. This decision depends on several factors such as cost, transit time, reliability, mode availability, service frequency, and cargo characteristics. However, existing research often focuses on only two modes, namely trucks and trains, which fails to capture the complexities of real-world freight transportation decisions. Moreover, while reducing travel time and cost are primary objectives for service providers and researchers, other important considerations such as environmental impact are often overlooked. To this end, in this work, the researchers take into account four major modes of transportation (Air, Road, Rail, and Sea/Water) in a multimodal freight context aiming to optimize three distinct objectives: overall transportation cost, transportation time, and CO2 emissions. To solve this problem, the researchers adopt two the well-known metaheuristic algorithms: Tabu Search and the Genetic Algorithm through an experimental study demonstrating the efficacy of these evolutionary solution methods in tackling such challenging optimization problems.