• Joint optimisation for routing and data transmission under three heterogeneous drone demands. • Synchronised data transmission between drones when direct depot connection is lost. • A MIQCP model formulation is proposed and then solved by ALNS algorithm. • Case study shows cost savings under certain drone and cost configurations. Drones are emerging as a promising tool in urban areas for tasks like monitoring, data collection, and deliveries. While each of these tasks has been studied independently in previous research, there have been very few studies on the possible combination of multiple types of services and their joint optimisation. Since different services can potentially occur within the same geographical area, and drones can be equipped with various tools to simultaneously handle different tasks, joint optimisation has the potential to reduce costs while maintaining service quality. This study proposes and examines the joint optimisation of multi-service capabilities within urban applications using multi-function drones. Our approach includes equipping drones with urban monitoring, wireless communication, data collection and storage systems, as well as parcel mounting frames. These drones can undertake (i) Real-time urban monitoring, (ii) Data collection, and (iii) One-to-one pickup and delivery parcel services. We consider constraints like battery, payload capacity, service time windows, data storage capacity, wireless connectivity, and synchronised data inter-transmission. The problem is modelled as a Mixed Integer Quadratically Constrained Program to optimise drone routes and data transmission strategies for cost minimisation. We also introduce an Adaptive Large Neighbourhood Search heuristic algorithm for solving large-scale problems. Numerical studies are conducted on a toy network to evaluate the algorithm and on a regional network in Hong Kong to showcase the efficacy of utilising multi-function drones.
Lyu et al. (2026) studied this question.