Advancing Dynamic Relief Response: Integration of New Data Streams and Routing Models

这一研究项目的目标是(1)评估危机期间可用的新数据流如何改善后勤决策，以及(2)促进数据流融入人道主义后勤行动，如搜救(SAR)和救济分发工作。自2010年1月海地发生毁灭性地震以来，人们越来越多地使用和更深入地了解新技术在救灾中的作用，以及将这些技术纳入应对活动的挑战。该项目的成果将是侧重于人道主义后勤的综合数据来源分类，以及编制用于人道主义紧急情况的实时数据集的必要程序的正式说明。此外，这项工作将确定动态路由模型中需要的改进，以适应这些新的数据流和现场复杂的决策集。实地活动将把本科生/研究生、教师和实践者聚集在一起。最终，该项目将为该领域的研究人员提供一个框架，以促进这一领域的工作。该项目还确定了一类新的路线模型，这些模型结合了定向运动和路径规划问题，需要解决这些独特的问题设置的挑战。将新出现的数据流纳入人道主义后勤，如搜救行动，有可能挽救生命，最大限度地减少受自然灾害和其他危机影响的人们的痛苦。随着从业人员、跨学科研究小组、人道主义组织和更广泛的危机绘制界之间持续进行一系列对话，在实践中成功创建和实施模型的可能性将增加。这项研究还将使本科生和研究生接触到运筹学知识的新应用，从而对他们产生影响。

The goals of this research project are to (1) assess how new data streams available during a crisis can improve logistical decisions, and (2) facilitate data stream integration into humanitarian logistics operations, such as search-and-rescue (SAR) and relief distribution efforts. Since the devastating January 2010 Haitian earthquake, there has been an increasing use and a deeper understanding of the role of new technologies in disaster relief, as well as the challenges of integrating these technologies into response activities. The deliverables of this project will be a comprehensive data source taxonomy focused on humanitarian logistics and a formal description of the necessary processes to prepare real-time datasets for use in humanitarian emergencies. Furthermore, this work will identify needed advances in dynamic routing models to accommodate these new data streams and the complex set of decisions in the field. Field-based activities will bring together undergraduate/graduate students, faculty and practitioners.Ultimately, this project will provide a framework for researchers in the field to further this line of work. The project also identifies a new class of routing models that combine orienteering and path planning problems needed to address the challenges of these unique problem settings. Integration of emerging data streams into humanitarian logistics, such as SAR operations, has potential to save lives and minimize suffering of people affected by natural disaster and other crises. The likelihood of success in model creation and implementation in practice will increase with a series of ongoing conversations between practitioners, inter-disciplinary research teams, humanitarian organizations and the broader crisis mapping community. This research will also impact undergraduate and graduate students by exposing them to new applications of their operations research knowledge.