Optimization-based Learning for Dynamic Load Planning in Trucking
  Service Networks
Ritesh Ojha Wenbo Chen Hanyu Zhang Reem Khir Alan Erera Pascal Van Hentenryck
The load planning problem is a critical challenge in service network design
for parcel carriers: it decides how many trailers (or loads) to assign for
dispatch over time between pairs of terminals. Another key challenge is to
determine a flow plan, which specifies how parcel volumes are assigned to
planned loads. This paper considers the Dynamic Load Planning Problem (DLPP)
that considers both flow and load planning challenges jointly to adjust loads
and flows as the demand forecast changes over time before the day of
operations. The paper aims at developing a decision-support tool to inform
planners making these decisions at terminals across the network. The paper
formulates the DLPP as a MIP and shows that it admits a large number of
symmetries in a network where each commodity can be routed through primary and
alternate paths. As a result, an optimization solver may return fundamentally
different solutions to closely related problems, confusing planners and
reducing trust in optimization. To remedy this limitation, the paper proposes a
Goal-Directed Optimization that eliminates those symmetries by generating
optimal solutions staying close to a reference plan. The paper also proposes an
optimization proxy to address the computational challenges of the optimization
models. The proxy combines a machine learning model and a feasibility
restoration model and finds solutions that satisfy real-time constraints
imposed by planners-in-the-loop. An extensive computational study on industrial
instances shows that the optimization proxy is around 10 times faster than the
commercial solver in obtaining the same quality solutions and orders of
magnitude faster for generating solutions that are consistent with each other.
The proposed approach also demonstrates the benefits of the DLPP for load
consolidation, and the significant savings obtained from combining machine
learning and optimization.