Journal of Transportation Research

Journal of Transportation Research

Multi-Objective Optimization of Truck Platooning Considering Time Window and Different Speeds

Document Type : Original Article

Authors
Hosein Firouzi 1
Javad Rezaeian 2
Alireza Rashidi Komijan 3
Mohammad Meddi Movahedi 4
1 Ph.D., Candidate, Faculty of Management, Islamic Azad University, Firouzkouh Branch, Iran.
2 Associate Professor, Department of Industrial Engineering, Faculty of Management and Industrial Engineering, University of Science and Technology of Mazandaran, Babol, Iran.
3 Associate Professor, Department of Industrial Engineering, Islamic Azad University, Firouzkouh Branch, Iran.
4 Associate Professor, Department of Management, Islamic Azad University, Firouzkouh Branch, Iran.
10.22034/tri.2025.466575.3258
Abstract
Platooning means moving trucks in a sequence on the road. In this method, a group of vehicles can safely and at high speed move together as a string, which can save fuel consumption, reduce greenhouse gas emissions, and use road capacity more efficiently. This is because a truck that is ahead of the other truck(s) has less air drag and less drag on the vehicles behind. Since in a platoon, the distance between vehicles is short and the vehicles move at high speed, an adaptive cruise control system and proper planning are needed to avoid collisions and ensure safety. The establishment and development of platooning in transportation systems with heavy vehicles is profitable and useful because it reduces the cost of fuel as well as the amount of carbon emissions by these vehicles. In this research, we seek to present a multi-objective optimization model for the truck platooning problem by considering several objectives. We also intend to use meta-heuristic algorithms based on Pareto archive to solve the model. In this article, first, a two-objective mathematical model is presented for the studied problem, and then multi-objective particle swarm algorithms, NSGA-II, and the epsilon constraint approach were used to solve the model. The model was solved for different sample problems and the results showed that the proposed particle mass algorithm has a higher ability to produce higher quality solutions and higher dispersion than the NSGA-II algorithm in all cases. Also, the NSGA-II algorithm produces solutions with higher uniformity in less time than the PSO algorithm.
Keywords
Rest of Drivers
Multi-Objective Optimization
Platooning of Trucks
Fuel Reduction
Subjects
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