CAREER: Next Generation Online Resource Allocation

该教师早期职业发展计划（CAREER）赠款将通过支持研究来研究资源动态分配的新模型和算法，从而促进国家繁荣和经济福利。这项工作将开发新颖实用的算法解决方案，以适应共享移动、电动汽车电池交换和在线广告等应用中出现的复杂功能、目标和约束。该研究将（i）系统的算法见解，导致实际的实施和更强大的在线平台;（ii）识别和利用在线资源分配问题与其他领域（如嵌入式理论）之间的联系，以及（iii）发现分析在线算法的新方法。伴随的教育计划旨在扩大STEM的兴趣，并通过培训社区大学教师在线资源分配和运营工程相关主题，以及合作开发社区大学课程的互动学习模块，为代表性不足的社区提供机会。该奖项支持的研究将为现代在线资源分配环境制定下一代模型，并设计直观的算法对于他们的解决方案，强调可扩展性，以大的问题实例和最好的理论性能保证。这将通过识别导致算法广泛适用和鲁棒性不断变化的环境的一般结构特性来实现。一个主要的技术重点将是开发方法来分析自适应在线算法，可以对问题实例中的随机不确定性的实现作出反应。自适应算法通常具有最佳的实际性能，但它们的理论分析具有挑战性，并且除了在某些特定设置中之外，人们对其知之甚少。结果将包括最坏情况下的性能界限和数值实验比较所提出的算法与国家的最先进的approaches.This奖反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.