Learning to Consolidate: Candidate-Pool Optimization for Freight Networks

We study freight planning in which indivisible shipments can share vehicles on scheduled departures. The large number of feasible paths makes joint optimization difficult, while choosing paths independently can miss opportunities to consolidate freight. We propose a learning-guided method that builds and expands a pool of candidate paths. A neural network scores departures using demand and network information. These scores are converted into shared search costs, under which a path subproblem is solved exactly for each shipment. A restricted mixed-integer master then jointly selects paths and vehicle counts using the original costs and constraints. Loads in the current solution guide the generation of additional paths, which are added to the pool before the master is solved again. On held-out public instances, three neural encoders reduce average cost by 2.54–2.84% and lower median runtime relative to analytic scores in the same search procedure under a common nominal time budget. A separate real-data development case illustrates the method’s computational performance at operational scale.