Integrated optimization of storage node location and multi-commodities flow routes in multimodal transport networks with carbon emissions consideration - Hu, Ziqiang; Wei, Yuguang; Li, Qi; Li, Chen (Energy (Oxford), 2025)
Multimodal transportation offers significant advantages in reducing energy consumption and carbon emissions for long-distance freight. The location of cargo storage nodes significantly impacts total costs and carbon emissions, as different site selections lead to variations in transportation distance and storage prices. Therefore, the integrated storage location and multi-commodity flow routing problem (ISLRP) within multimodal transport networks is crucial for minimizing total costs, including transportation, transshipment, storage, and carbon emissions costs. ISLRP is a variant of the routing and flow allocation problem faced by multimodal transport operators (MTOs). Operators must consider the storage locations, storage durations, and transportation routes for all customer shipments to minimize total costs while effectively meeting shipping demands within specified deadlines. The multimodal transport network under consideration involves various transportation modes, such as road, railway, and waterway transport. By topologically reconstructing the multimodal transport network, ISLRP directly incorporates storage processes both before dispatch and after arrival, enabling integrated optimization of storage and transportation for multi-commodities. This study evaluates the effectiveness of the ISLRP model through a case study of a multimodal transport network in China, providing MTOs more flexible solutions for ISLRP while meeting customer demands. To further assess the policy relevance and model sensitivity, we investigate the effects of varying carbon pricing scenarios. When the carbon price increases from US$50 to US$100 per ton, carbon emissions decrease by 2.72 %–12.09 % compared to the baseline at US$27.35 per ton, indicating that carbon pricing policies have a significant impact on ISLRP result. •Integrated optimization of storage node location and route planning.•Freight node and surrounding storage nodes together form the storage network.•Impact of carbon emission costs on multimodal transport route planning.•Guiding role of carbon pricing strategies in green logistics.•Linearization of the mixed-integer programming model.