Optimization Models and Algorithms for Emergency Response Planning

应急响应规划的优化模型和算法

• 批准号: 0728334
• 负责人: Fernando Ordonez
• 金额: $ 25万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0728334&HistoricalAwards=false
• 关键词: Optimization; Models; Algorithms; Emergency; Response

This project will develop better plans for an effective deployment of medical supplies in response to a large-scale infectious disease outbreak. The multiple decisions involved in an efficient logistics response compounded with the uncertainty present leads to large-scale optimization/decision problems that are intractable using current methods. The proposed research will create models that provide robust solutions to the uncertainty present and develop adaptable decomposition and branching algorithms for large-scale mixed integer nonlinear programs. These algorithms will use new results on sensitivity measures for problems under uncertainty, and warm start strategies for interior point methods.Recent events, such as the 2004 Indian Ocean Tsunami and the 2005 Hurricane Katrina, have highlighted the massive impact that large-scale emergencies can inflict on society. Careful planning can optimize the logistics response to such emergencies reducing their impact on the population. This project will develop new modeling methods and algorithms that will enable the development of better plans for the disbursement of medical supplies in response to an infectious disease outbreak. Ultimately, improving preparedness can help save lives in emergencies. The methods developed here can be applied to improving preparedness in other problems and, in addition, the optimization techniques developed can be applied to solve other convex mixed integer programs. This project includes an outreach component to local, state and federal stakeholders through the Governmental Advisory Committee of the CREATE Research Center, a Department of Homeland Security funded research center at USC. This project will lead to curricular developments in logistics and optimization, involve a Ph.D. student, and involve minority undergraduate students in summer research projects through USC's McNair Scholar's Program.

该项目将制定更好的计划，有效部署医疗用品，以应对大规模传染病爆发。 在一个有效的物流响应中涉及的多个决策与目前的不确定性相结合，导致大规模的优化/决策问题，使用当前的方法是棘手的。 拟议的研究将创建模型，提供强大的解决方案，目前的不确定性和开发适应性的分解和分支算法的大规模混合整数非线性规划。 这些算法将使用新的结果的敏感性措施的问题下的不确定性，和热启动策略的内点方法。最近的事件，如2004年印度洋海啸和2005年卡特里娜飓风，突出了大规模的紧急情况可能对社会造成的巨大影响。 仔细规划可以优化对此类紧急情况的后勤反应，减少其对民众的影响。 该项目将开发新的建模方法和算法，从而能够制定更好的计划，以应对传染病爆发时的医疗用品支出。 最后，改进准备工作有助于在紧急情况下拯救生命。 这里开发的方法可以应用于提高准备在其他问题，此外，开发的优化技术可以应用于解决其他凸混合整数规划。 该项目包括通过南加州大学国土安全部资助的研究中心CREATE研究中心的政府咨询委员会向地方、州和联邦利益攸关方进行宣传。 该项目将导致物流和优化课程的发展，涉及博士学位。学生，并通过南加州大学的麦克奈尔学者计划在夏季研究项目涉及少数民族本科生。