Optimization of Ambulance Services Sequencing and Scheduling Daily Decisions with Minimizing Delay

Ambulances, one of the essential resources in the emergency medical service (EMS), are crucial in transporting patients to hospitals and saving their lives. This research considers an ambulance services scheduling problem (ASSP) for a daily planning decision by minimizing the total weighted tardiness. A mixed integer linear mathematical model for the research problem is developed. Since the research problem is shown to be NP-hard, genetic algorithm (GA) and particle swarm optimization (PSO) are proposed. Random test problems with small, medium, and large sizes are generated and solved using GA and PSO algorithms to evaluate their performance. The experiment results show that the GA algorithm performs better in the small size problem, while the PSO algorithm performs better solutions in medium and large sizes. Due to the ASSP problem's stochastic environment, we develop a two-stage stochastic programming model by considering each mission's weight and time under uncertainty. Finally, a case study is implemented based on real-world data, and the numerical results show the worth of the stochastic model. In addition, considering enough scenarios, which in our research is 20, the optimal value may be closely approximated