Objective In recent years, the refined oil market has approached saturation. As a result, oil sales companies have increasingly focused on optimizing multimodal transport logistics. Their goal is to develop a robust system that reduces consignment costs, enhances the timeliness of transport plans, and improves responsiveness to market fluctuations within the primary logistics framework for refined oil.

Methods This study addresses key deficiencies, such as low solution efficiency caused by manual parameter adjustments and iterations, as well as significant deviations between pipeline transportation plans and actual conditions resulting from excessively broad time window divisions. To overcome these challenges, a daily dispatching optimization model for multimodal transport of refined oil is developed, enabling segmentation optimization while ensuring that the oil depots’ demands are consistently met. Grounded in an integral philosophy and daily dispatching framework, the model features an objective function that minimizes the sum of the total logistics costs, including both non-pipeline and pipeline transport expenses, and penalty costs incurred from shortages at the oil depots. It also accounts for the characteristics of “batch sequential” pipeline transportation and “containerized” transportation via railway, waterway, and road. Furthermore, the model fully incorporates operational and business constraints relevant to the formulation of refined oil logistics plans and pipeline dispatching plans, covering loading and unloading capacities at terminals, corridor transport capacities, batch migrations, and injection/off-take operations at stations. Consequently, the model can consider in-transit conditions across various transportation modes, including transport times and in-transit plans at the start and end of each month, thereby supporting the creation of more practical primary logistics plans for refined oil.

Results The developed daily dispatching optimization model for multimodal transport of refined oil was applied to optimize dispatching based on monthly logistics plans in a region of China. The results demonstrated that the model automatically generated solutions for the large-scale complex logistics system—including two pipelines—effectively eliminating the need for human intervention in the solution process. The model’s outputs included refinery delivery plans, oil depot receiving plans, “batch sequential” pipeline transportation plans, “containerized” transportation plans for other modes (railway, waterway, road), terminal transfer plans, and terminal inventory plans, all accompanied by time information. Incorporating in-transit conditions into the dispatching plans resulted in a 16％ increase in transfer volume and a 2％ reduction in costs compared to plans that did not consider these conditions.

Conclusion The established model facilitates coordinated resource allocation, batch dispatching for pipeline transport, and multimodal transport, effectively eliminating human intervention and efficiency losses associated with iterative solutions. As a result, it supports dispatching decisions for large-scale complex systems by generating logistics solutions with greater practicality.