REU Site: Research on Prescriptive Analytics for AI-enabled Operations Engineering

REU网站的目标是填补学生在规范分析方面的知识空白，这是一种分析形式，为如何利用未来的机会或降低未来的风险提出决策选择。规范性分析对于服务业、制造业和农业等经历高支出、供需错配和运营不确定性的关键行业尤为关键。该REU项目不仅将为学生提供所需的知识，还将为他们提供进行研究的技能，以便为涉及数据驱动的规划和决策的现实世界问题提供智能/可扩展的说明性分析解决方案。此外，通过区域教育联盟的项目，许多未被充分代表的少数群体、妇女和第一代毕业生将接受独立研究方面的培训，这反过来又将激励他们攻读STEM高级学位。暑期项目将培养学生对开发几项创造性和新奇研究工作的兴趣，例如：(I)实时调度模型，以优化无人地面车辆(UGV)-转移机器人(也称为。(Ii)创建医疗流程改进的区块链系统；(Iii)设计基于分解的迭代优化算法以动态调度和布线无人机(即无人机和空中出租车)；以及(Iv)使用深度学习了解基因、环境和管理(G×E×M)交互。总之，让学生接触人工智能支持的运营工程规范分析中的多学科研究项目，灌输对科学和工程的热情，并培养他们追求高级STEM学位的演示技能是拟议的REU网站的主要目标。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this REU site to fill the gap in student knowledge in prescriptive analytics, a form of analytics which suggests decision options for how to take advantage future opportunities or mitigate future risks. Prescriptive analytics are particularly critical for key industries such as services, manufacturing and agriculture that experience high expenditure, supply-demand mismatch, and operational uncertainties. This REU program will not only equip students with the required knowledge but provide them the skillset to conduct research on delivering intelligent/scalable prescriptive analytics solutions to real-world problems involving data-driven planning and decision making. In addition, through the REU projects, numerous underrepresented minorities, women and first-generation graduates, will be trained in doing independent research, which will, in turn, motivate them to pursue advanced degrees in STEM. The summer program will cultivate student interest in developing several creative and novel research works, e.g., (i) real-time scheduling models to optimize the planning and control of unmanned ground vehicle (UGV) - transfer robots (a.k.a. transbots), (ii) creating a Blockchain system for healthcare process improvement, (iii) designing decomposition-based iterative optimization algorithms for dynamically dispatching and routing unmanned aerial vehicles (UAVs), i.e., drones and air taxis, and (iv) understanding genotype, environment and management (G×E×M) interaction using deep learning. In summary, exposing students to multidisciplinary research projects in prescriptive analytics for AI-enabled operations engineering, inculcating a passion for science and engineering, and cultivating their presentation skillsets for pursuing advanced STEM degrees are the main goals of the proposed REU Site.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.