A bi-objective optimization model and solution approach for emergency evacuation planning under pandemic settings

<h2>Abstract</h2> Effective emergency evacuation planning is crucial for ensuring safety and efficiency of transportation networks during natural disasters. In the meantime, this planning is challenging, as large populations have to be evacuated in a short span of time. Moreover, additional challenges can be imposed by ongoing pandemics due to increased virus transmission risk in overcrowded emergency shelters. This study presents an innovative bi-objective mixed integer programming model for optimizing emergency evacuation under pandemic conditions, focusing on assigning individuals, including vulnerable populations, to emergency shelters through designated evacuation routes within specified time periods. The model uniquely addresses two critical objectives: minimizing the total evacuation time and minimizing the risk of virus transmission in shelters. To solve this complex problem efficiently, a novel decomposition-based epsilon-constraint (DECON) algorithm is developed. This approach overcomes the computational limitations often encountered in large-scale evacuation scenarios, allowing for the generation of Pareto-optimal solutions that provide a range of evacuation strategies balancing timely evacuations and acceptable virus transmission risk in shelters. The model's efficacy is demonstrated through the numerical experiments conducted for Palm Beach County, Florida. The results indicate that the DECON algorithm successfully handles large-scale problem instances, providing high-quality solutions within reasonable computational times. Moreover, interesting managerial insights regarding utilization of evacuation routes and emergency shelters are provided as well. The proposed optimization model and algorithm can serve as valuable tools for emergency management, enabling the development of efficient and timely evacuation plans that account for both traditional evacuation concerns and the unique challenges posed by ongoing public health crises.