Communication-aware Drone Delivery Problem

The drone delivery problem (DDP) has been introduced to include aerial vehicles in last-mile delivery operations to increase efficiency. However, the existing studies have not incorporated the communication quality requirements of such a delivery operation. This study introduces the communication-aware DDP (C-DDP), which incorporates handover and outage constraints into the conventional multi-depot multi-trip green vehicle routing problem with time windows. In particular, any trip of a drone to deliver a customer package must require less than a certain number of handover operations and cannot exceed a predefined outage duration threshold. A mixed integer programming (MIP) model is developed to minimize the total flight distance while satisfying communication constraints. We present a genetic algorithm (GA) that can solve large instances and compare its performance with an off-the-shelf MIP solver. Computational study shows that the GA and MIP solver performances are equivalent to solving smaller instances. We also compare the GA performance against another evolutionary algorithm, particle swarm optimization (PSO), for larger instances and find that the GA outperforms the PSO with slightly longer CPU times. The results indicate that ignoring the communication constraints would cause significant operational disruption risk and this risk can be easily mitigated with a slight sacrifice from flight distances by incorporating the proposed communication constraints. In particular, the communication performance can be improved by up to 28.9% when the flight distance is increased by 19.1% at most on average.