BRITE Relaunch: Compact Network Flows for Critical Infrastructure Engineering

网络无处不在。我们的社会每天都依赖于能源、交通和通信网络。 网络的设计和运行是一个跨越许多工程学科的主要问题。这个促进工程变革和公平进步的研究思路（BRITE）重新启动奖利用了网络流数学建模的新范式，以实现（1）开发更快，更可扩展的算法来解决大型，运输中的复杂路由问题（2）设计具有弹性的电信网络，即使在关键组件损坏时也能够继续提供高水平的服务。 如果成功，该项目将大大提高我们设计和运营物流和通信关键基础设施网络的能力。从历史上代表性不足的群体在工程本科研究助理将被招募工作的补充活动，旨在向公众展示该项目的成果，并促进运营工程的未来学生谁否则可能不知道该领域。 研究助理将与学生和志愿者谁是目前从事与首席研究员在一个类似的activity.The新的范式大大减少了变量和约束的数量相比，标准的节点弧表示整数规划模型的网络优化问题的多样化的社区。这反过来又可以导致更快的解决方案算法。该项目的目标是使用该范式开发（1）用于固有困难的网络优化问题的精确算法，这些问题比现有方法在更短的时间内找到可证明的最佳解决方案，以及（2）快速找到高质量但不一定是最佳解决方案的算法。 解决方案的方法将包括切割平面派生专门为新的配方和该项目将测试一个假设，即新模型的结构允许一个专门的实施的单纯形方法。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

Networks are ubiquitous. Every day our society relies on networks for energy, transportation, and communication. The design and operation of networks is a major concern that cuts across many engineering disciplines. This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Relaunch award leverages a new paradigm for the mathematical modeling of network flow to enable (1) the development of faster and more scalable algorithms for solving large, complex routing problems in transportation and (2) the design of resilient telecommunication networks that have the ability to continue to provide a high level of service even when critical components are damaged. If successful, this project will significantly improve our ability to design and operate critical infrastructure networks for logistics and communications. Undergraduate research assistants from historically underrepresented groups in engineering will be recruited to work on a complementary activity designed to demonstrate outcomes of the project to the general public and to promote Operations Engineering to prospective students who might otherwise not be aware of the field. The research assistants will interact with a diverse community of students and volunteers who are currently engaged with the principal investigator in a similar activity.The new paradigm significantly reduces the number of variables and constraints in integer programming models for network optimization problems compared to the standard node-arc representation. This in turn can lead to faster solution algorithms. The goals of the project are to use the paradigm to develop (1) exact algorithms for inherently difficult network optimization problems that find provably optimal solutions in less time than existing methods, and (2) heuristics that quickly find solutions that are high-quality, but not necessarily optimal. Solution methodology will include cutting planes derived specifically for the new formulations and the project will test a hypothesis that the structure of the new model allows for a specialized implementation of the Simplex Method.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.