Infrastructure Systems Planning of Integrated Preparedness Logistics Networks for Large-Scale Foreseen Disasters

对可预见的自然灾害作出有效反应需要在战略意义上作出认真的基础设施发展决定，并在疏散和救济供应活动的战术意义上协调规划/管理工作。这种协调的复杂性在最近发生的2017年哈维和厄玛飓风事件以及2005年卡特里娜和丽塔飓风事件中变得明显，这些事件使人们意识到现有应对计划的缺陷。这项研究的动机是认识到在即将来临的飓风准备计划活动的决策过程中所涉及的利益相关者及其个人目标。正如在实际情况中经常看到的那样，涉众相互冲突的动机可能导致由个人目标驱动的决策，而不是在系统视图下的最佳决策，并且，因此，导致意想不到的后果。造成这种情况的一个主要原因是，目前的紧急后勤模式在很大程度上忽视了撤离（需求）和救济（供应）双方之间重要的相互依存关系。这笔赠款将从应急准备规划的成本效益和业务效益两方面对这种整合进行调查。如果成功，这笔赠款的成果将有助于今后为应急后勤规划人员开发复杂和易于使用的决策支助工具。这将有助于将服务带给更广泛的公民群体，使其有可能在即将发生的灾难中有效地离开危险的方式。来自不同背景和代表性不足群体的本科生和研究生将参与并接受最先进的工具和手段的培训，以进行具有社会影响力的研究。虽然过去几十年取得了重大进展，但在综合框架内理解和利用供需双方之间联系的全面努力还不存在。特别是在存在不确定性的情况下，从飓风特征到疏散决策的供应规划，疏散人员的行为在采用集成系统框架时提出了优化的主要挑战。这项拨款解决了理论和实践中的这一关键差距。这项工作将首次系统地探索在应急准备/响应物流的基础设施设计和运营规划中整合供需双方。整个系统将在优化框架内的两层决策中进行考虑：战略基础设施相关和战术/操作决策，通过基于潜在灾害事件和撤离人员行为的不确定性明确建模。因此，总的来说，研究的组成部分充分捕获了利益相关者的活动，而不是像现有文献中通常做的那样独立地计划他们的行动，本研究旨在提高系统层面的效率和成本效益。该方法汇集了新的视角和目的，以研究不确定性下新的和具有挑战性的优化模型，并为其最优解提供解决方案算法，它还将建立一个框架，通过以可比的方式解决当前决策的优化问题，来检查集成优于现有方法的好处。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Effective response to foreseen natural disasters requires careful infrastructure development decisions made in strategic sense and coordination of planning/management efforts in the tactical sense for evacuation and relief supply activities. The complexity of such coordination became obvious in the recent events of Hurricanes Harvey and Irma in 2017 as well as earlier ones, such as Katrina and Rita in 2005, which created significant awareness in the shortcomings in existing response plans. This research is motivated by the recognition of the stakeholders involved and their individual objectives in the course of decision-making for preparedness planning activities upon an imminent hurricane. As commonly witnessed in real situations, conflicting motivations of stakeholders can lead to decisions driven by individual objectives rather than what is best under a system view, and, thus, lead to unintended consequences. One main reason for this occurrence is the current paradigm of emergency logistics that has largely overlooked the important interdependence between evacuation (demand) and relief (supply) sides. This grant will investigate such integration from the perspective of both cost- and operation-effectiveness in emergency preparedness planning. If successful, the outcome of this grant will facilitate future development of sophisticated and easy-to-use decision support tools for emergency logistics planners. It will help to bring services to a wider group of citizens by making it possible to move out of harm’s way efficiently in case of an impending disaster. Undergraduate and graduate students from diverse backgrounds and underrepresented groups will be engaged and trained with state-of-the-art tools and means to conduct socially impactful research. While significant advances have been made in past decades, comprehensive efforts to understand and leverage the linkage between demand and supply sides within an integrated framework do not exist. Especially in the presence of uncertainty that carries from hurricane characteristics to supply planning through evacuation decision-making and evacuee behavior presents a major challenge in optimization when employing an integrated systems framework. This grant addresses this critical gap in theory and practice. This work will be the first to systematically explore the integration of demand and supply sides in infrastructure design and operations planning for emergency preparedness/response logistics. The overall system will be considered in two layers of decisions within an optimization framework: strategic infrastructure related and tactical/operational decisions by explicitly modeling uncertainties based on potential disaster events and evacuee behavior. Thus, collectively, the components of the research fully capture the stakeholder activities, and rather than planning for their actions independently, as is commonly done in existing literature, this research aims to improve efficiency and cost-effectiveness at the system level. The methodology brings together new perspectives and purpose to investigate new and challenging optimization models under uncertainty and solution algorithms for their optimal solutions and it will also establish a framework to examine the benefits of integration over the existing approaches by addressing optimization of current decision-making in a comparable manner.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.