2025

Conférence

Selecting the most reliable stochastic routes requires the development of flexible, real-time, single-objective and multi-objective approaches based on technology and data analysis. In the

Abstract—The increasing complexity of global supply chains,
combined with the need for fast, cost-effective, and environmentally
friendly deliveries, has reinforced the importance of
multimodal freight transportation(MFT) as a key solution to
meet modern demands. One of the main challenges in MFT
is to develop an innovative optimization model to plan and
manage the supply chain. In this work, we consider four
modes of transportation (air, road, rail, and sea) and propose
an innovative multi-objective optimization model, designed to
simultaneously minimize transportation costs, transit times,
and CO2 emissions, while integrating the complex operational
constraints inherent in current logistic systems. To address
this problem, we adopt two well-known algorithms : Non-
Dominated Sorting Genetic Algorithm III (NSGA-III) and
Teaching-Learning Optimization (TLBO), through an experimental
study demonstrating the effectiveness of these evolutionary
solution methods in solving these complex optimization
problem.
The results show that TLBO optimization effectively reduces
costs and environmental impact, while the NSGAIII algorithm
improves delivery times.

Mokhtar.L, Lilia.R and Lamhed.s, (2025). Ecological Multimodal Freight Transport Optimization (IEE conference CoDit 2025).