Multi-class freight tour synthesis model incorporating environmental, entropy, cost, and travel time objectives

This research develops a multi-objective and multiclass freight tour synthesis transportation model. The model integrates objectives of maximizing trip entropy while minimizing costs and time, including reducing CO₂ emissions. The study identified various solutions along the Pareto frontier and evaluated the impact of other constraints on costs, emissions, and time using the epsilon-constraint method. The results show that entropy favors a balanced distribution of resources, while time prioritizes the use of higher-capacity diesel trucks. Minimizing emissions prioritizes electric trucks, highlighting the trade-off between sustainability and operational efficiency. The TOPSIS multicriteria method was used to rank or prioritize the solutions. This method depends on the weight assigned to each objective; thus, a sensitivity analysis of the weights was conducted. The solutions reflect the necessary trade-offs between costs, time, emissions, and system diversity. It is concluded that incorporating environmental and entropy objectives in fleet optimization improves sustainability, operational flexibility, and adaptability.