ABSTRACT
This study investigates a novel emergency medical services scheduling problem (EMSSP) to address the outbreak of epidemics like COVID-19. The objective is to determine an optimal scheduling scheme to minimize the emergency service time for nucleic acid testing (NAT) and achieve rapid epidemic disruption. For the problem, we fifirst formulate the EMSSP into a mixed-integer linear programming (MILP) and analyze its complexity is NP-hard. However, due to the NP-hardness of the problem, existing optimization software such as CPLEX is diffiffifficult to solve large-scale problems in an acceptable time.To effiffifficiently address the EMSSP, we design two effffective improved artifificial bee colony algorithms (IABC) based on explored problem properties. Then, numerical experiments on a real-life case and randomly generated large-scale instances with up to 100 demand points are conducted. Finally, computational results show that the IABC algorithms can obtain a better scheduling scheme than CPLEX and two state-of-the-art evolutionary algorithms in only 16 seconds for large-scale instances, which helps decision-makers deal with the outbreak of epidemics in a short time.