Collaborative Research: Advancing Supply Prepositioning for Emergency Preparedness by Social Sensing

合作研究：通过社会感知推进应急准备供应预置

• 批准号: 1662865
• 负责人: Young-Jun Son
• 金额: $ 15万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1662865&HistoricalAwards=false
• 关键词: Collaborative; Research; Advancing; Supply; Prepositioning

Foreseen disasters, such as hurricanes, snowstorms and droughts, can be detected several days prior to their landfall. To ameliorate the consequences of these disasters, humanitarian and governmental agencies can utilize forecast information to conduct preparedness activities. However, one unresolved challenge is to obtain accurate estimation of spatial-temporal heterogeneous supply demand for supply prepositioning. To tackle this challenge, this research plans an approach which integrates data from social media and simulation to predict demand for humanitarian supplies. Resulting models and prototype tools are expected to contribute to support for improving communication with the public, for optimizing supply prepositioning strategies, and for facilitating decision-making to help prevent or ameliorate the impacts of extreme disasters. This research will also lead to new educational modules and software useful for teaching undergraduate and graduate students how to design, analyze and implement a humanitarian logistics system. This research will investigate the potential for integrating social media data and agent-based simulation to reduce uncertainty associated with the demand for post-disaster relief supplies. This project will develop a new social sensing approach based on the integration of social media analysis and agent-based simulation. Trustworthy information will be extracted from social media by a new spatio-temporal Bayesian method. Second, this project will build a new forecast-driven multi-commodity supply prepositioning model that incorporates Markovian and decision theoretic perspectives within a stochastic optimization framework. Third, the outcome of the supply prepositioning will be realized by social delivery, an emerging delivery scheme that leverages delivery power from both fleet and volunteers' vehicles. Different optimization decomposition techniques will be examined and developed to handle the large-scale nature of the considered problem. To test the validity of the approaches, a range of case studies will be conducted.

飓风、暴风雪和干旱等可预见的灾害可以在登陆前几天被检测到。为了减轻这些灾害的后果，人道主义和政府机构可以利用预报信息来开展备灾活动。然而，一个尚未解决的挑战是准确估计时空异构供应需求以进行供应预置。为了应对这一挑战，这项研究计划采用一种方法，整合社交媒体和模拟的数据来预测人道主义物资的需求。由此产生的模型和原型工具预计将有助于支持改善与公众的沟通、优化供应预置策略以及促进决策以帮助预防或减轻极端灾害的影响。这项研究还将推出新的教育模块和软件，可用于教授本科生和研究生如何设计、分析和实施人道主义物流系统。这项研究将调查整合社交媒体数据和基于代理的模拟的潜力，以减少与灾后救援物资需求相关的不确定性。该项目将开发一种基于社交媒体分析和基于代理的模拟相结合的新的社交感知方法。可信信息将通过新的时空贝叶斯方法从社交媒体中提取。其次，该项目将建立一个新的预测驱动的多商品供应预设模型，该模型将马尔可夫和决策理论观点纳入随机优化框架内。第三，供应预置的结果将通过社会配送来实现，这是一种新兴的配送方案，利用车队和志愿者车辆的配送能力。将检查和开发不同的优化分解技术来处理所考虑问题的大规模性质。为了测试这些方法的有效性，将进行一系列案例研究。

项目成果

A Hierarchical Decision-making Process in Social Networks for Disaster Management

灾害管理社交网络中的分层决策过程

• 发表时间: 2018
• 期刊: IISE Annual Conference and Expo 2018
• 作者: Lee, Seunghan;Son, Young-Jun
• 通讯作者: Son, Young-Jun

Dynamic-data-driven agent-based modeling for the prediction of evacuation behavior during hurricanes

• DOI: 10.1016/j.simpat.2020.102193
• 发表时间: 2021
• 期刊: Simul. Model. Pract. Theory
• 作者: Seunghan Lee;Saurabh Jain;Keeli Ginsbach;Y. Son

Extended decision field theory with social-learning for long-term decision-making processes in social networks

• DOI: 10.1016/j.ins.2019.10.025
• 发表时间: 2020-02
• 期刊: Inf. Sci.
• 作者: Seunghan Lee;Y. Son