Large-Scale Cargo Distribution
This work addresses the challenge of transforming rigid large logistics infrastructure into dynamic, just-in-time delivery operations for logistics operators, but it is incremental as it builds on existing graph-based methods.
The study tackled the problem of generating cargo distribution plans for large-scale logistics networks by proposing a three-step methodology that partitions the infrastructure graph into regions and creates partial plans, enabling scaling to thousands of drop-off locations with better scalability than state-of-the-art methods while maintaining solution quality.
This study focuses on the design and development of methods for generating cargo distribution plans for large-scale logistics networks. It uses data from three large logistics operators while focusing on cross border logistics operations using one large graph. The approach uses a three-step methodology to first represent the logistic infrastructure as a graph, then partition the graph into smaller size regions, and finally generate cargo distribution plans for each individual region. The initial graph representation has been extracted from regional graphs by spectral clustering and is then further used for computing the distribution plan. The approach introduces methods for each of the modelling steps. The proposed approach on using regionalization of large logistics infrastructure for generating partial plans, enables scaling to thousands of drop-off locations. Results also show that the proposed approach scales better than the state-of-the-art, while preserving the quality of the solution. Our methodology is suited to address the main challenge in transforming rigid large logistics infrastructure into dynamic, just-in-time, and point-to-point delivery-oriented logistics operations.