Multimodal Transport Scheme Optimization and Capacity Allocation Considering Customer Classification

Abstract

Although multimodal transport has been widely developed, the multimodal transport operation system is still not as mature as other single modes of transport. Currently, the optimization of multimodal transport solutions and the allocation of capacity are planned as two separate parts, and the research on customer classification of multimodal transport is insufficient. The current bottleneck in multimodal transportation systems is optimizing multimodal customer classification and transportation solutions. Additionally, such an optimization can significantly improve the system’s overall revenue. Based on the customer classification theory of revenue management, we classified and managed multimodal transport customers and allocated and priced the transportation capacity according to the demand characteristics and price sensitivities of different customers. Furthermore, we considered the impact of transportation scheme planning on the profit of multimodal transport. Accordingly, we developed a joint optimization model for multimodal transport schemes, capacity allocation, and pricing by considering the customer classification. We solved the model using a hybrid particle swarm algorithm and validated it using arithmetic examples. The results indicate that a customer classification strategy can significantly improve the profit of multimodal transport operators when customer demand is unstable, and the total profit of the transportation system is increased by 7%. Moreover, these results suggest combining transportation solution optimization with customer classification can lead to more profitable multimodal revenue management solutions. Thus, our results can guide multimodal operators for systematically dealing with the customer classification problem and optimizing transportation schemes while improving their profits. Moreover, this study offers a reference for decision-making in multimodal operation management.