Real-time optimization for relocation and dispatching of Emergency Medical Services with balanced workload and outsourced ride-hailing services

Abstract

Real-time optimization of Emergency medical services (EMS) leads to excessive and unbalanced workload unless balance is directly modeled. Online redeployment policies aim to reduce response times, compensating for the coverage gap left by busy ambulances, however, it is important to account for the additional effort required to relocate to a new position. Additionally, irregular demand patterns result in unequal distributions of workload among the personnel. In this regard, we propose a new survival-based real-time optimization framework that maximizes the survival probability of patients suffering from life-threatening emergencies while maintaining a balanced workload among the crew. Our approach implements an optimization model inside a discrete event simulation model that feeds the former with real-time vehicle utilization metrics to maintain an optimal overall relocation policy. Moreover, we evaluate the impact on the system’s congestion of a mixed dispatched policy that includes external ride-hailing services for minor emergencies. Our case study in New York City indicates that the proposed model increases the average percentage of life-threatening emergencies responded in less than four minutes in more than 10% through the use of survival functions compared to a coverage model from the literature. Furthermore, even though the fleet’s aggregated driving distances increased compared to the coverage approach, our online model effectively balanced the additional workload among the crew. Additionally, ride-hailing services’ introduction presents an important opportunity for reducing personnel workload by 13.5%, on average, and operational costs in $141,220 per day of operation for the entire system. Finally, when ambulance availability is severely reduced, outsourced services significantly help maintaining the response level for low severity emergencies.