Holistic Modeling and Multi-Objective Optimization of Emergency Ambulance Dispatching

Efficient emergency responses are vital during disasters to minimize casualties. The chaotic nature of such events requires optimized ambulance dispatching. Due to the NP-hard nature of this problem, Swarm Intelligence algorithms are the most effective choice.This paper presents a comprehensive formulation for the emergency ambulance dispatching problem, integrating multiple problem definitions that have traditionally been addressed separately in the literature. The proposed modeling of the problem optimizes multiple objectives simultaneously, such as response time, travel distance, and priority, providing flexibility to adapt to specific parameters and user preferences based on the disaster scenario. We introduce the Multi-Objective Discrete Flamingo Search Algorithm (MODFSA), an adaptation of the Flamingo Search Algorithm (FSA) for discrete multi-objective optimization.A case study on the COVID-19 pandemic in Algeria demonstrates MODFSA’s effectiveness. The results highlight MODFSA’s potential to enhance emergency medical services during disasters and emergency contexts in general.