Dynamic emergency routing problem for electric motorcycle fleets under uncertain conditions considering battery swapping

Most Emergency Medical Service (EMS) systems worldwide face the challenge of reducing response times from the occurrence of an incident to patient arrival. Responding to patient demands is highly time-sensitive, leading EMS providers to use motorized vehicles. Motorized ambulances carry battery-dependent medical devices such as automated external defibrillators (AEDs). In this context, employing electric motorcycles not only ensures rapid transportation but also facilitates continuous charging of onboard medical devices throughout their routes. In addition, EMS demands are unpredictable, and new requests may dynamically occur while vehicles are en route to provide services. Therefore, dynamic response capability becomes critically important. To address these issues, this study proposes a MILP dynamic emergency routing problem model for electric motorcycle fleets considering battery swapping stations. The proposed model integrates routing decisions for both patients and battery swapping stations to effectively manage response time. The model is capable of dynamically receiving new patient demands and updating optimal routes in real-time upon information changes. Furthermore, the model includes soft time windows, and patient service time is treated as uncertain. Small-scale instances are solved using exact methods, while larger-scale problems are addressed through a Variable Neighborhood Search (VNS) algorithm. Numerical experiments demonstrate that the proposed algorithm can obtain solutions with 98% accuracy, operating much faster than exact solutions. The results indicate that the proposed model significantly reduces EMS response time, enhances dynamic response capabilities, and effectively accounts for uncertainty, thereby improving the effectiveness and efficiency of emergency medical operations in realistic conditions.