A single neural model can now handle multiple variants of complex routing problems by dynamically adapting to different constraints, suggesting that multi-task learning with adaptive conditioning is more practical than building separate models for each problem type.
FiLMMeD is a neural model that solves 24 different multi-depot vehicle routing problems (a logistics optimization task) using a single unified architecture.