  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.
