CAREER: Next Generation Online Resource Allocation

职业：下一代在线资源分配

• 批准号: 2340306
• 负责人: Rajan Udwani
• 金额: $ 56.22万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2029-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340306&HistoricalAwards=false
• 关键词: CAREER; Next; Generation; Online; Resource

This Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national prosperity and economic welfare by supporting research to study new models and algorithms for dynamic allocation of resources. This work will develop novel, practical algorithmic solutions that can accommodate the complex features, objectives, and constraints arising in applications such as shared mobility, battery swapping for electric vehicles and online advertising. The research will (i) systematic algorithmic insights leading to practical implementations and more robust online platforms; (ii) identify and exploit connections between online resource allocation problems and other areas such as queueing theory, and (iii) enable discovery of new methods for analyzing online algorithms. The accompanying educational plan aims to broaden STEM interest and to provide opportunities for underrepresented communities by training community college instructors on topics related to online resource allocation and operations engineering and collaboratively developing interactive learning modules for community college courses.The research supported by this award will formulate the next generation of models for modern online resource allocation environments and design intuitive algorithms for their solution, emphasizing scalability to large problem instances and the best possible theoretical performance guarantees. This will be accomplished by identifying general structural properties that lead to algorithms that are broadly applicable and robust to changing environments. A major technical emphasis will be on developing methods to analyze adaptive online algorithms that can react to realization of stochastic uncertainties in the problem instance. Adaptive algorithms typically have the best practical performance, but their theoretical analysis is challenging and poorly understood except in some specific settings. Results will include worst case performance bounds and numerical experiments comparing the proposed algorithms with state-of-the-art approaches.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.

这项教师早期职业发展计划（职业）赠款将通过支持研究来研究新的模型和算法以进行动态分配资源，从而为国家繁荣和经济福利的发展做出贡献。这项工作将开发出新颖的实用算法解决方案，这些解决方案可以容纳在共享移动性，电动汽车和在线广告等应用程序中产生的复杂功能，目标和约束。研究将（i）系统的算法见解，从而实现实际实施和更强大的在线平台； （ii）确定并利用在线资源分配问题与其他领域（例如排队理论）之间的联系，以及（iii）启用用于分析在线算法的新方法。随附的教育计划旨在通过培训社区大学讲师与在线资源分配和运营工程相关的主题，并为社区大学课程提供协作的互动性学习模块，为该奖项提供的研究将为现代资源分配的模型和设计量大的范围，以确定其范围的范围，以宣传他们的范围和设计型号的范围，该研究将针对现代化的环境和设计量表，该研究的范围为他们的范围划分，并强调了他们的范围，以使他们的现代化环境和设计的范围为他们的范围提出，请注意他们的范围，并强调他们的范围，该研究的范围是针对他们的现代化环境和设计，该研究的范围是针对现代化的，并强调他们的范围，以使他们的现代化环境和设计的范围逐渐提高他们的互动式学习模块。理论性能保证。这将通过确定导致算法的一般结构属性来实现，这些算法广泛适用且适用于不断变化的环境。主要的技术重点将是开发方法来分析可以对问题实例中随机不确定性实现的反应的自适应在线算法。自适应算法通常具有最佳的实践性能，但是除了某些特定的环境外，它们的理论分析具有挑战性且知之甚少。结果将包括最坏的案例性能范围和数值实验，将所提出的算法与最先进的方法进行比较。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估评估来支持的。