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CAREER: An Adaptive Stochastic Look-ahead Framework for Disaster Relief Logistics under Forecast Uncertainty

职业生涯：预测不确定性下救灾物流的自适应随机前瞻框架

基本信息

批准号：
2045744
负责人：
Yongjia Song
金额：
$ 50万
依托单位：
Clemson University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2026-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045744&HistoricalAwards=false
关键词：
CAREER Adaptive Stochastic Look ahead

项目摘要

This Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national health, prosperity and welfare by contributing new knowledge on effective disaster relief logistics operations for advance-notice natural disasters such as hurricanes and slow-moving storms. Improved disaster relief efforts can both alleviate human suffering and reduce economic loss. Current disaster relief logistics planning and operations do not effectively incorporate evolving weather forecasts and natural hazard analysis tools. This project will address this shortcoming by creating adaptive decision-support methods for effectively staging and utilizing scarce resources, leveraging both real-time forecast information and historical data. This project will foster a long-term collaboration between the operations research community and emergency management agencies by designing novel logistics decision support tools. The accompanying educational program aims to enrich engineering curriculum with data-driven analytic tools, create interdisciplinary research opportunities, and develop outreach activities for K-12 students and the general public to help them understand the role of operations research in addressing critical societal challenges such as disaster relief logistics.This research will contribute a holistic modeling and algorithmic framework for sequential decision making in disaster relief logistics planning and operations under dynamically evolving disaster situations and their rolling forecasts. This project will: (i) establish new theory to understand the impact of evolving forecast uncertainty on the quality of the decision policy induced by past forecast information; (ii) produce novel algorithms that integrate offline and online stochastic programming models using adaptive sampling, state space approximation, and stage approximation within a rolling-horizon procedure; and (iii) create and analyze novel structured decision policies to address the need to coordinate the timing of various logistics operations with heterogeneous modalities. The modeling and solution methodology on disaster relief logistics operations planning will be validated using both historical data on past hurricanes and simulation data. Research results will help engage and inform emergency managers in making logistics planning and operational policies that balance between adaptability, optimality and executability in practice.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.

这项教师早期职业发展计划（Career）资助将通过为飓风和缓慢移动的风暴等提前通知的自然灾害提供有效的救灾后勤行动的新知识，为促进国家健康、繁荣和福利做出贡献。加强救灾工作既能减轻人类痛苦，又能减少经济损失。目前的救灾后勤规划和运作没有有效地纳入不断发展的天气预报和自然灾害分析工具。该项目将通过创建自适应决策支持方法来解决这一缺点，从而有效地利用稀缺资源，同时利用实时预测信息和历史数据。该项目将通过设计新颖的后勤决策支持工具，促进运筹学研究界和应急管理机构之间的长期合作。配套的教育项目旨在用数据驱动的分析工具丰富工程课程，创造跨学科的研究机会，并为K-12学生和公众开展拓展活动，帮助他们了解运筹学在解决关键社会挑战（如救灾后勤）中的作用。本研究将提供一个整体的模型与演算法框架，以协助救灾物流规划与作业在动态演化的灾害情势与滚动预测下的序贯决策。本项目将：(i)建立新的理论来理解不断变化的预测不确定性对由过去预测信息引起的决策政策质量的影响；（ii）在滚动地平线过程中使用自适应采样、状态空间近似和阶段近似，产生集成离线和在线随机规划模型的新算法；（iii）创建和分析新颖的结构化决策政策，以解决协调各种异构模式的物流操作时间的需求。灾害救援物流行动规划的建模和解决方法将使用过去飓风的历史数据和模拟数据进行验证。研究结果将有助于应急管理人员参与并为他们提供信息，帮助他们制定物流规划和业务政策，在实践中平衡适应性、最优性和可执行性。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。