A multi-objective fuzzy facility location problem with congestion and priority for drone-based emergency deliveries

Emergency medical service (EMS) plays an essential role in modern emergency and health systems; however, the delivery service via traditional ground vehicles faces enormous challenges, e.g., traffic congestion and poor road conditions, especially for time-critical products. Fortunately, drones provide an alternative solution for EMS due to faster speed, fewer road restrictions, and fewer workforce requirements, compared to ground vehicles. This paper develops a drone-based queuing-location model with stochastic demands and congestion for EMS, where drones perform as mobile servers with a generally distributed service time. In practice, delivery decisions are often made in the presence of imprecise information, and customer requests often have different priorities. However, to our best knowledge, limited research has addressed these issues. Thus, this paper employs fuzzy theory to cope with the vague drone endurance and demand arrival rate under a priority queuing strategy. A multi-objective optimization approach is adopted to balance the total cost, system efficiency, and equitable response time. As the resulting model is challenging to solve, we apply chance-constrained, second -order conic, fuzzy, and weighted goal programming approaches to recast the model as a crisp mixed-integer second-order conic program, which can be efficiently solved via off-the-shelf solvers. Results based on a case study show that our method can help decision-makers to better balance various objectives, make more flexible decisions with desirable fuzzy degrees, and significantly improve the service level of high priority demands for EMS.