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1. Integrated Hurricane Relief Logistics and Evacuation Planning under Forecast Uncertainty: A Case Study for Hurricane Florence

   Bhattarai, Sudhan; Song, Yongjia (July 2023, Proceedings of the IISE Annual Conference & Expo 2023)

   Babski-Reeves, K.; Eksioglu, B.; Hampton, D. (Ed.)

   In this paper, we study an integrated hurricane relief logistics and evacuation planning (HRLEP) problem. We propose stochastic optimization models and methods that integrate the hurricane relief item pre-positioning problem and the hurricane evacuation planning problem, which are often treated as separate problems in the literature, by incorporating the forecast information as well as the forecast errors (FE). Specifically, we fit historical FE data into an auto-regressive model of order one (AR-1), from which we generate FE realizations to create evacuation demand scenarios. We compare a static decision policy based on the proposed stochastic optimization model with a dynamic policy obtained by applying this model in a rolling-horizon (RH) procedure. We conduct a preliminary numerical experiment based on real-world data to validate the value of stochastic optimization and the value of the dynamic policy based on the RH procedure. 

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2. A stochastic optimization model for patient evacuation from health care facilities during hurricanes

   https://doi.org/10.1016/j.ijdrr.2024.104518  

   Kim, Kyoung Yoon; Toplu-Tutay, Gizem; Kutanoglu, Erhan; Hasenbein, John J (June 2024, International Journal of Disaster Risk Reduction)

   We propose a rigorous modeling and methodological effort that integrates statistical implementation of hydrology models in predicting inland and coastal flood scenarios due to hurricanes and a scenario-based stochastic integer programming model which suggests resource and staging area decisions in the first stage and the evacuation decisions in the second stage. This novel study combines physics-based flood prediction models and stochastic optimization for large- scale multi-facility coordination of hospital and nursing home evacuations before impending hurricanes. The optimization model considers scenario-dependent evacuation demand, transport vehicles with varying capacities, and both critical and non-critical patients. Utilizing Hurricane Harvey of 2017 as a case study and actual healthcare facility locations in southeast Texas, we explore various evacuation policies, demonstrating the impact of routing strategies, staging area decisions, flood thresholds, and receiving facility capacities on evacuation outcomes. One of the findings is that choosing staging area(s) and deploying evacuation vehicles optimally considering the uncertainty of the hurricane’s path at the time of decision making could have significant effect on the total cost of the operation and evacuation time experienced by the evacuees. We also show the non-negligible value of the scenario-based staging and routing solution conservatively calculated in relation to a single scenario solution using the concept of value of stochastic solution. 

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3. A Large-Scale Patient Evacuation Modeling Framework using Scenario Generation and Stochastic Optimization

   Kim, Kyoung Yoon; Kutanoglu, Erhan; Hasenbein, John J; Wu, Wen-Ying; Yang, Zong-Liang (January 2020, IISE Annual Conference)

   null (Ed.)

   This paper proposes a two-stage stochastic mixed integer programming framework for patient evacuation. While minimizing the expected total cost of patient evacuation operations, the model determines the location of staging areas and the number of emergency medical service (EMS) vehicles to mobilize in the first stage, and the EMS vehicle routing assignments in the second stage. A real-world data from Southeast Texas region is used to demonstrate our modeling approach. To provide a more pragmatic solution to the patient evacuation problem, we attempt to create comprehensive hurricane instances by integrating the publicly available state-of-art hydrology models for surge, Sea, Lake Ocean and Overland Surge for Hurricanes (SLOSH), and for streamflow, National Water Model (NWM), prediction. The surge product captures potential flooding in coastal region while the streamflow product predicts inland flooding. The results exhibit the importance of the integrated approach in patient evacuation planning, provide guidance on flood mapping and prove the potential benefit of comprehensive approach in scenario generation. 

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4. Stochastic Optimization of Large-Scale Patient Evacuation Before Hurricanes

   Kim, Kyoung Yoon; Kutanoglu, Erhan; Hasenbein, John J. (January 2019, IISE Annual Conference)

   null (Ed.)

   The total cost for weather-related disasters in the US increases over time, and hurricanes usually create the most damage. One of the challenges, which is present in almost every major hurricane event, is the patient evacuation mission. We propose a comprehensive modeling and methodological framework for a large-scale patient evacuation problem when an area is faced with a forecasted disaster such as a hurricane. In this work, we integrate a hurricane scenario generation scheme using publicly available surge level forecasting software and a scenario-based stochastic integer program to make decisions on patient movements, staging area locations and positioning of emergency medical vehicles with an objective of minimizing the total expected cost of evacuation and the setup cost of staging areas. The hurricane scenario generation scheme incorporates the uncertainties in the hurricane intensity, direction, forward speed and tide level. To demonstrate the modeling approach, we apply real-world data from the Southeast Texas region in our experiments. We highlight the importance of operation time limits, the number of available resources and an accurate forecast on forthcoming hurricanes in determining the locations of staging areas and patient evacuation decisions. 

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5. FLEE 2.0: An Improved Agent-Based Model of Hurricane Evacuations

   https://doi.org/10.1175/WCAS-D-24-0112.1  

   Harris, Austin; Morss, Rebecca; Davis, Chris; Roebber, Paul; Boehnert, Jennifer (October 2025, Weather, Climate, and Society)

   Abstract For this study, we present and evaluate an improved agent-based modeling framework, the Forecasting Laboratory for Exploring the Evacuation-system, version 2.0 (FLEE 2.0), designed to investigate relationships between hurricane forecast uncertainty and evacuation outcomes. Presented improvements include doubling its spatial resolution, using a quantitative approach to map real-world data onto the model’s virtual world, and increasing the number of possible risk magnitudes for wind, surge, and rain risk. To assess model realism, we compare FLEE 2.0’s simulated evacuations—specifically its evacuation orders, evacuation rates, and traffic—to available observational data collected during Hurricanes Irma, Dorian, and Ian. FLEE 2.0’s evacuation response is encouraging, given that FLEE 2.0 responds reasonably and differently to all three different types of forecast scenarios. FLEE 2.0 well represents the spatial distribution of observed evacuation rates, and relative to a lower spatial resolution version of the model, FLEE 2.0 better captures sharp gradients in evacuation behaviors across the coastlines and metropolitan areas. Quantitatively evaluating FLEE 2.0’s evacuation rates during Irma establishes model errors, uncertainties, and opportunities for improvement. In summary, this paper increases our confidence in FLEE 2.0, develops a framework for evaluating and improving these types of models, and sets the stage for additional analyses to quantify the impacts of forecast track, intensity, and other positional errors on evacuation. Significance StatementThis paper describes and evaluates an updated version of a modeling system [the Forecasting Laboratory for Exploring the Evacuation-system, version 2.0 (FLEE 2.0)] designed to explore relationships between hurricane forecasts and evacuation impacts. FLEE 2.0’s simulated evacuations compare favorably with different types of observational evacuation data collected during Hurricanes Irma, Dorian, and Ian. A statistical comparison with Irma’s observed evacuation rates highlights uncertainties and opportunities for improvement in FLEE 2.0. In summary, this paper increases our confidence in FLEE 2.0, develops a framework for evaluating these types of models, and provides a foundation for additional work using FLEE 2.0 as a research tool. 

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