  Supply chain transportation operations often account for a large proportion
of product total cost to market. Such operations can be optimized by solving
the Logistics Service Network Design Problem (LSNDP), wherein a logistics
service provider seeks to cost-effectively source and fulfill customer demands
of products within a multi-echelon distribution network. However, many
industrial settings yield instances of the LSNDP that are too large to be
solved in reasonable run-times by off-the-shelf optimization solvers. We
introduce an exact Benders decomposition algorithm based on partial
decompositions that strengthen the master problem with information derived from
aggregating subproblem data. More specifically, the proposed Meta Partial
Benders Decomposition intelligently switches from one master problem to another
by changing both the amount of subproblem information to include in the master
as well as how it is aggregated. Through an extensive computational study, we
show that the approach outperforms existing benchmark methods and we
demonstrate the benefits of dynamically refining the master problem in the
course of a partial Benders decomposition-based scheme.
