Transportation 4.0 planning in emergency medical services considering real-time ambulance two-phase...

Transportation 4.0 enables smart planning in urban and interurban transportation networks by leveraging intelligent technologies, such as Internet of Things (IoT), cloud computing, information system integration, and artificial intelligence, to facilitate real-time vehicle assignment and routing. Nevertheless, Transportation 4.0 planning involving vehicle smart assignment and routing, particularly in healthcare logistics, has been less addressed in the literature. However, this study takes an initial step toward intelligent planning of emergency medical services by proposing an Intelligent Emergency Information System (IEIS) for real-time ambulance assignment and routing. As part of data analytics in the designed IEIS, an integer linear mathematical model is proposed to optimize total travel times in the ambulance assignment and routing problem based on deterministic real-time input data in two phases: reaching the patients and transporting them to hospitals. The proposed mathematical model is solved in a case study of Amol city, Iran, incorporating 25 street nodes, nine ambulance nodes, six call nodes, and five hospital nodes, using the CPLEX solver. Although designing and implementing the suggested conceptual model of IEIS was not applicable in the case study, to evaluate the impact of real-time parameter updates, two scenarios involving real-time inclusion of a new call node and an ambulance breakdown are examined. Accordingly, the model was re-solved, producing updated ambulance assignments and routing in each scenario, even by rearranging the ambulances' missions. Results validated the flexibility of the proposed mathematical model in optimizing two-phase ambulance assignment and routing under real-time input updates.