Multi-Objective Multimodal Transportation Path Selection Based on Hybrid Algorithm

To address the problem of multi-objective and multimodal transport path selection, a mixed integer programming model is constructed by integrating transportation cost, transportation time, and logistics service quality. The optimization goal is to minimize transportation cost and transportation time and maximize logistics service. Owing to the different priorities and needs of customers, the weights of the three targets are determined by using a hybrid algorithm that combines genetic algorithm and ant colony algorithm, to solve the model. Taking Xi'an-Berlin path as an example, the analysis result of the hybrid algorithm is compared with those of the genetic algorithm and the ant colony algorithm. The results show that when the customer pays more attention to time and cost, the hybrid algorithm, genetic algorithm, and ant colony algorithms require 164, 170, and 183 iterations, respectively, to obtain the optimal solution. In this case, the optimal route is Xi'an-Zhengzhou(railway)-Dalian(railway)-Rotterdam (waterway)-Berlin(railway). When the customer pays more attention to the time and logistics service quality, the hybrid, genetic, and ant colony algorithms require 112, 117, and 150 iterations, respectively, to obtain the optimal solution, and the optimal route is Xi'an-Chongqing(highway)-Berlin (railway). When the customer pays more attention to the cost and logistics service quality, the hybrid, genetic, and ant colony algorithms require 115, 120, and 160 iterations, respectively, to obtain the optimal solution, and the optimal route is Xi'an-Zhengzhou(railway)-Shenzhen(railway)-Rotterdam(waterway)-Berlin (railway). The research shows that both models and hybrid algorithms can effectively provide practical optimization schemes and route references for multi-objective multimodal transport path selection problems when setting different target weights. © 2019, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.