Collaborative optimization of routing and storage strategy of multi-period multimodal transport in an uncertain environment

Abstract

This study uses a storage service strategy to investigate a multiperiod multimodal transport routing and transit planning problem (MPMT-RTP). First, a mathematical integer programming model is used to describe the problem in a deterministic environment. Second, two robust optimization models are established to address the uncertainty of the freight rate fluctuations in each period and the storage space in the total set, thereby improving the solving efficiency of the model through a robust equation transformation. Subsequently, hybrid heuristics are proposed to solve the above problems, and a series of small-scale experiments are conducted to verify the accuracy of the model and effectiveness of the algorithm. Finally, this study conducts case experiments based on survey data. The results show that the total operating cost is reduced by 13.31 % via strategic multiperiod multimodal transport planning. A price fluctuation study shows that under different price fluctuations, the transportation and storage periods of some orders change accordingly, and under three specific price fluctuations, the operating costs are 1085100, 967330, and 948770, respectively. Therefore, the results of this study can provide theoretical guidance for enterprises to optimize their resource allocation paradigms and better adapt to freight rate fluctuations to minimize their operating costs.