Logistics transportation path optimization based on priority theory and improved NSGA-II algorithm

With the rapid increase in transportation vehicles, traffic congestion has become a major factor affecting logistics efficiency. To address this issue, this study introduces priority theory into logistics transportation scheduling. By considering factors such as urgency, importance, and customer satisfaction, transportation tasks are ranked and prioritized to identify optimal multi-objective path solutions in complex logistics environments. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is enhanced through the integration of Simulated Binary Crossover and Gaussian Mutation to construct a logistics transportation path optimization model. Comparative experiments demonstrate that the proposed model achieves the lowest anti-generation distance (0.07) and distribution index (0.005) with stable convergence. In terms of on-time performance, the model maintains 100% punctuality within 30 days, outperforming comparison algorithms. Furthermore, its generation gap (0.010), distribution breadth (0.008), inverse generation gap (0.084), and hypervolume (0.978) closely match those obtained using Lingo software, indicating high accuracy and robustness. Experimental results confirm that the proposed optimization model effectively enhances logistics transportation efficiency by accurately determining optimal routes and demonstrates strong adaptability to complex and dynamic logistics conditions.