Multi-objective Logistics Transportation Optimization based on Multiple Transportation Modes via Evolutionary Algorithm

Logistics transportation optimization represents a significant challenge within the optimization field. However, previous research has predominantly concentrated on single-objective, single-modal transportation systems aimed at minimizing economic costs, often neglecting the crucial aspect of user satisfaction. To address this issue, this study develops a comprehensive optimization model that simultaneously minimizes economic cost and maximizes user satisfaction, while integrating multiple transportation modes—including highway, railway, and airway—as decision variables. By employing an evolutionary algorithm to generate scheduling solutions, the experimental results demonstrate the model’s effectiveness in reducing economic costs, enhancing user satisfaction, and enhancing system robustness.