  Efficient algorithms and solvers are required to provide optimal or
near-optimal solutions quickly and enable organizations to react promptly to
dynamic situations such as supply chain disruptions or changing customer
demands. State-of-the-art mixed-integer programming (MIP) solvers are crafted
to tackle a wide variety of problems, yet many real-world situations are
characterized by problem instances that originate from a narrow distribution.
This has inspired the creation of tailored approaches that exploit historical
data to inform heuristic design. Deep learning (DL) methods are typically used
in this context to extract patterns from data, but they require large datasets
and comprehensive hyperparameter tuning for strong performance. This article
describes a one-shot learning heuristic that leverages solutions discovered
within the branch-and-bound tree to construct a model with minimal overhead. We
evaluate our method on the locomotive assignment problem (LAP) and sets of
MIPLIB instances that contain constraints based on special ordered sets of type
1. Experimental results include a comparison with multiple primal heuristics
and state-of-the-art MIP solvers. We show that the method is most effective
with CPLEX in terms of the average primal gap.
