Abstract
Intermodal transport involving both land and sea encompasses multiple stakeholders and objectives, often leading to mutually exclusive goals and discretely distributed optimal solutions. This complexity makes it challenging to generate stable intermodal transport schemes. To address this, the study proposes a method using Pareto correlation structure analysis to determine the stable solution set for multi-objective optimization in land-sea intermodal transport. By defining the Pareto solution space and employing dimensionality reduction and cluster analysis, a structured correlation model is constructed. This method utilizes projection and transformation operators based on stakeholder preferences to generate a stable Pareto solution set. Using the Shanghai-Qingdao intermodal transport as a case study, the Pareto solution set is generated through a multi-objective particle swarm optimization algorithm. Based on the preferences of various stakeholders, including consignors, carriers, and environmental departments regarding transportation objectives, a multi-stakeholder stable solution set is obtained. The results indicate that, compared to schemes derived through weighted summation, the transportation schemes constructed using the proposed method balance stakeholders' multi-objective requirements, improving overall stability by approximately 30% to 60%. This approach generates stable optimized candidate schemes, facilitating the management of complex intermodal transport scenarios and supporting the selection of multi-stakeholder optimization solutions.
Published Version
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