Freight consolidation has significant potential to reduce transportation costs and mitigate congestion and pollution. An effective load consolidation plan relies on carefully chosen consolidation points to ensure alignment with existing transportation management processes, such as driver scheduling, personnel planning, and terminal operations. This complexity represents a significant challenge when searching for optimal consolidation strategies. Traditional optimization-based methods provide exact solutions, but their computational complexity makes them impractical for large-scale instances and they fail to leverage historical data. Machine learning-based approaches address these issues but often ignore operational constraints, leading to infeasible consolidation plans. This work proposes SPOT, an end-to-end approach that integrates the benefits of machine learning (ML) and optimization for load consolidation. The ML component plays a key role in the planning phase by identifying the consolidation points through spatio-temporal clustering and constrained frequent itemset mining, while the optimization selects the most costeffective feasible consolidation routes for a given operational day. Extensive experiments conducted on industrial load data demonstrate that SPOT significantly reduces travel distance and transportation costs (by about 50%) on large terminals) compared to the existing industry-standard load planning strategy and a neighborhood-based heuristic. Moreover, the ML component provides valuable tactical-level insights by identifying frequently recurring consolidation opportunities that guide proactive planning. In addition, SPOT is computationally efficient and can be easily scaled to accommodate large transportation networks.

Abstract In order to make accurate judgements of rolling bearing main fault types using the small sample size fault data set, a novel approach is put forward that combines particle swarm optimisation kernel fuzzy C‐means (PSO‐KFCM) and variational mode decomposition (VMD). Firstly, by calculating the centre frequency and Pearson correlation coefficient of each mode function of VMD, the decomposition level K of VMD is determined, and the optimal decomposition result is obtained. The singular value decomposition method was used to extract a characteristic value corresponding to the main fault types of bearings from the optimal decomposition results, and faulty feature sample space was established. Then, the kernel function parameters and the initial clustering centre were used as optimisation variables. The PSO algorithm was used to solve the clustering model. The clustering centre of each fault type under the optimal classification result was obtained, and the fault diagnosis model was established. Finally, different fault classification methods are compared, and the conclusions drawn from the experiment show that the method can achieve good results in bearing fault diagnosis. The accuracy of fault classification was improved obviously.