The stochastic location-routing problem with parallel truck–drone operations for humanitarian aid delivery

Timely response in the aftermath of a disaster is crucial to alleviate loss of life and suffering. Timeliness of relief may be hampered by road network disruptions caused by the disaster, such as damage to road segments or debris covering the roads. The use of drones simultaneously with trucks can potentially help overcome issues around network disruptions and achieve more timely delivery of post-disaster aid. In an effort to shed more light into this potential, we address the problem of network design for parallel truck–drone operations by depot location prior to the disaster and routing of the vehicles in its aftermath. We incorporate the uncertainty on network disruption by modelling this problem as a two-stage stochastic program, which proves computationally challenging to solve to optimality for real-life disaster scenarios. Consequently, we propose a tailored heuristic based on variable neighbourhood search to find high-quality solutions efficiently. Our computational results on randomly generated instances and a case study from the 2011 Van Earthquake in Turkiye demonstrate the effectiveness of the heuristic, the benefits of employing both trucks and drones, and the significance of accounting for uncertainties in pre-disaster planning. • Network design and for a parallel truck–drone routing to deliver humanitarian aid. • Considers uncertain travel times due to network vulnerability and latency objective. • Introduces a flow-based formulation for sparse networks lacking triangle inequality. • Develops a tailored VNS for scenario-specific depot location and routing. • Applies model on a case study to draw managerial insights.