LIFTing UAV Schedules: Diffusion Policies for Iterative Improvement

Unmanned aerial vehicle (UAV) scheduling is a challenging sequential decision problem with tightly coupled operational constraints. Even small local edits can propagate through payload, energy, and timing dependencies, making high-quality schedules difficult to obtain through one-shot generation. We instead cast it as a sequential refinement process and propose LIFT, a refinement-aligned one-step diffusion policy for Learning schedule improvement via Imitation and PPO Fine-Tuning. LIFT predicts structured edits on a schedule-dependent candidate graph, decodes them into feasible successor schedules, and optimizes them for long-horizon improvement. Training follows a refinement-aligned two-stage design: it first learns edit-level behavior from expert improvement trajectories, and then applies PPO fine-tuning through a one-step denoiser with tractable conditional action log-probabilities. Trained only on small-scale instances, LIFT achieves the lowest mean total arrival time across all evaluated benchmark configurations and generalizes to substantially larger problems without retraining. Together, these results reposition diffusion in combinatorial optimization: from a one-shot solution generator to a reinforcement learning policy for sequential solution refinement.