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（职业）赠款将通过为预先通知自然灾害（例如飓风和缓慢移动的风暴）提供有效的救灾物流运营的新知识，从而为国家健康，繁荣和福利的发展做出贡献。改善的救灾工作既可以减轻人类的痛苦，又可以减少经济损失。当前的救灾后勤计划和运营没有有效地纳入不断发展的天气预测和自然危害分析工具。该项目将通过创建自适应决策支持方法来解决这一缺点，以有效地分阶段和利用稀缺资源，从而利用实时预测信息和历史数据。该项目将通过设计新颖的物流决策支持工具来促进运营研究社区与紧急管理机构之间的长期合作。随附的教育计划旨在通过数据驱动的分析工具来丰富工程课程，创造跨学科的研究机会，并为K-12学生和公众开发外展活动，以帮助他们了解运营研究在解决灾难救济等疾病救援方面的关键社会挑战中的作用，以解决灾害的依据，以促进整体策略，以促进整体的模型和实现的范围，以促进整体的模型和实现的范围，以实现整体的促进，以促进整体的临时型号，以实现整体的策略，以实现促进疾病的范围，以促进序列化的范围，以促进整体的促进式促进型号，以促进整体的临时型号，以实现整体的核对范围，以促进临时的序列范围内的范围，以实现整体的促进式侵害。灾难情况及其滚动预测。该项目将：（i）建立新理论，以了解不断预测的不确定性对过去预测信息引起的决策政策质量的影响；（ii）生成新颖的算法，这些算法在滚动马过程中使用自适应采样，状态空间近似和阶段近似来整合离线和在线随机编程模型；（iii）制定和分析新型的结构化决策政策，以解决与异构方式协调各种物流运营时间的需求。关于救灾物流运营计划的建模和解决方案方法将使用过去的飓风和模拟数据的历史数据进行验证。研究结果将有助于参与并告知应急管理人员，以制定物流规划和运营政策，在实践中适应性，最佳性和可执行性之间取得平衡。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子的智力优点和更广泛的影响来通过评估来支持的。