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The load planning problem is a critical challenge in service network designfor parcel carriers: it decides how many trailers (or loads) to assign fordispatch over time between pairs of terminals. Another key challenge is todetermine a flow plan, which specifies how parcel volumes are assigned toplanned loads. This paper considers the Dynamic Load Planning Problem (DLPP)that considers both flow and load planning challenges jointly to adjust loadsand flows as the demand forecast changes over time before the day ofoperations. The paper aims at developing a decision-support tool to informplanners making these decisions at terminals across the network. The paperformulates the DLPP as a MIP and shows that it admits a large number ofsymmetries in a network where each commodity can be routed through primary andalternate paths. As a result, an optimization solver may return fundamentallydifferent solutions to closely related problems, confusing planners andreducing trust in optimization. To remedy this limitation, the paper proposes aGoal-Directed Optimization that eliminates those symmetries by generatingoptimal solutions staying close to a reference plan. The paper also proposes anoptimization proxy to address the computational challenges of the optimizationmodels. The proxy combines a machine learning model and a feasibilityrestoration model and finds solutions that satisfy real-time constraintsimposed by planners-in-the-loop. An extensive computational study on industrialinstances shows that the optimization proxy is around 10 times faster than thecommercial solver in obtaining the same quality solutions and orders ofmagnitude faster for generating solutions that are consistent with each other.The proposed approach also demonstrates the benefits of the DLPP for loadconsolidation, and the significant savings obtained from combining machinelearning and optimization.

Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks