SGER: Multi-scale Modeling for Homeland Security and Supply-Chain Logistics Efficiency

这项探索性研究小额拨款（SGER）制定了多尺度空间建模程序，用于处理各种细节的数据，以支持复杂物流优化问题的解决方案。研究人员开发的可靠性概念在解决国土安全相关问题的物流领域有用。最后，本项目将概率统计模型与运筹学工具相结合，提高港口集装箱作业效率，满足安全检查的需要。安全可靠的供应链物流网络的评估和设计对现代美国经济至关重要。特别是，在运输物流系统和海港集装箱检查的设计和操作中平衡安全和效率目标是至关重要的，并且这样做提出了独特的数学和统计建模挑战。这个由统计学家和物流学家组成的跨学科团队将带来多年的研究和教育经验，以探索这一领域的潜力。该项目的关键科学影响是，该项目开发的理论和方法为多尺度数据建模和分层决策分析工具的进步奠定了基础。该项目还提供了NSF所要求的几个更广泛的影响，例如，与工业界合作传播研究成果，通过论文指导和课堂教学（本项目探讨了案例研究）培养高质量的学生，以及支持来自代表性不足群体的学生的暑期研究经验。

This Small Grant for Exploratory Research (SGER) formulates multi-scale spatial modeling procedures for handling data in various details to support solution of complex logistics optimization problems. The investigators develop reliability concepts useful in the logistics field for solving homeland security related problems. Finally, this project integrates probability and statistical models with operations research tools to improve seaport container-operation efficiency to meet the needs of security inspections.Evaluation and design of secure and reliable supply-chain logistics networks are of critical importance to the modern U.S. economy. In particular, balancing security and efficiency objectives in the design and operation of transportation logistics systems and seaport container inspections is vitally important, and to do so presents unique mathematical and statistical modeling challenges. This interdisciplinary team of statisticians and logisticians will bring years of experience in research and education to explore the potential in this field. The key scientific impact of this project is that the theory and methods developed in this project serve as the foundation for advances in multi-scale data modeling and hierarchical decision-analysis tools. This project also provides several broader impacts required by the NSF, e.g., collaboration with industry for disseminating research results, education of high-quality students through thesis advising and class teaching (with case studies explored in this project), and support of summer research experience for students from underrepresentated groups.