Ambulance routing in disaster response considering variable patient condition: NSGA-II and MOPSO algorithms

<p style='text-indent:20px;'>The shortage of relief vehicles capacity is a common issue throughout disastrous situations due to the abundance injured people who need urgent medical aid. Hence, ambulances fleet management highly important save as many individuals possible. In this regard, present paper defines different patient groups based on their needs and characteristics. order provide affected with proper timely aid, changes in health status are also considered. A Mixed-integer Linear Programming (MILP) model proposed find best sequence routes for each ambulance minimize latest service completion time (SCT) well number patients whose condition gets worse because receiving untimely services. Non-dominated Sorting Genetic Algorithm II (NSGA-II) Multi-Objective Particle Swarm Optimization (MOPSO) used high-quality solutions over short time. end, Lorestan province, Iran, considered case study assess model's performance analyze sensitivity respect major parameters, which results insightful managerial suggestions.</p>