Replenishment-Aware Operation Scheduling for a Single Asphalt Crack Repair Robot

Autonomous asphalt crack repair robots can reduce workers’ exposure to traffic and improve the repeatability of pavement maintenance, but their field productivity depends on operation-level scheduling as well as crack perception and sealing control. This paper formulates a single-robot asphalt crack repair problem in which all detected cracks in an operation zone must be repaired under finite sealant capacity and explicit replenishment returns. A compact mixed-integer programming model is developed, NP-hardness is established, and an adaptive large-neighborhood search with a dynamic-programming replenishment decoder (ALNS-DP) is proposed. The method separates crack-sequence search from exact replenishment-feasible segmentation for each candidate sequence. Twelve anonymized real operation-zone cases from Hefei, China, containing 18–64 cracks are used for evaluation, and six algorithms are run ten times on every case. Compared with the strongest stochastic baseline, ALNS-DP reduces the mean final objective by 5.11% on average, with case-level reductions ranging from 0.40% to 8.52%; on the representative Case-08, the objective reduction is 6.86%. Small exact MIP benchmarks with 8–12 cracks give 0.00% optimality gaps for ALNS-DP. Additional replenishment-location, material-demand-error, ablation, scalability, and statistical tests show that the main advantage comes from coupling sequence search with replenishment-aware decoding rather than from omitting difficult cracks.