CAREER: Improving Operational Decision Making with Predictive Information and Data

职业：利用预测信息和数据改进运营决策

• 批准号: 1944209
• 负责人: Jing Dong
• 金额: $ 50万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1944209&HistoricalAwards=false
• 关键词: CAREER; Improving; Operational; Decision; Making

This Faculty Early Career Development (CAREER) grant promotes the national health and welfare by studying methods to improve the efficiency of resource planning and operations in healthcare and other critical services. This award extends our understanding of data-driven methods that can provide better models of patient flow and staffing. With the growing availability of data and computational powers, there is an increasing awareness and appreciation of data-backed decision making. However, challenges remain in identifying what types of predictive information are most valuable, and subsequently in quantifying system performance. The findings will provide structural insights and effective policies to improve system efficiency, consumer experience, and quality of care. The educational mission aims to expose students to the challenges of improving operational efficiency and reducing risk in service and healthcare industries. The accompanying educational plan facilitates training of engineers and researchers who will be equipped with both solid theoretical backgrounds as well as practical insights in data-informed operations research. Through the educational activities, the Principal Investigator is committed to promoting the participation of underrepresented groups in engineering. This research contains two main threads. The first studies the use predictive information to achieve better system performance. The objective is to develop a theoretical framework to evaluate the effectiveness of different kinds of information and to quantify the impact of the information’s accuracy. Techniques from asymptotic analysis of stochastic processes will be extended to incorporate and analyze the impact of predictive information. Moreover, advanced data-informed policies that lead to substantial performance improvement and are robust to prediction errors will be constructed. The focus is on limited-resource environments where externalities need to be carefully modeled and studied. Motivated by healthcare applications, new paradigms for transient performance analysis and optimization in face of multiple scales of uncertainty will be developed. The second thread studies how to utilize data to build better stochastic models. The objective is to develop a data-driven modeling framework that provides accurate quantifications of salient tradeoffs in system operations. Key challenges in model calibration, causal inference, and performance evaluation will be addressed by combining tools from econometrics with stochastic modeling. Prescriptive solutions that can be readily implemented will be provided based on the high-fidelity nature of the models.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.

该学院早期职业发展（CAREER）补助金通过研究提高医疗保健和其他关键服务的资源规划和运营效率的方法来促进国家健康和福利。 该奖项扩展了我们对数据驱动方法的理解，这些方法可以提供更好的患者流量和人员配置模型。 随着数据和计算能力的不断增长，人们对数据支持决策的认识和理解越来越多。然而，在识别什么类型的预测信息是最有价值的，并随后在量化系统性能方面仍然存在挑战。 调查结果将提供结构性见解和有效的政策，以提高系统效率、消费者体验和护理质量。 教育使命旨在让学生接触到提高运营效率和降低服务和医疗保健行业风险的挑战。 随附的教育计划有助于培养工程师和研究人员，他们将具备坚实的理论背景以及数据知情的运筹学实践见解。通过教育活动，首席研究员致力于促进代表性不足的群体参与工程。本研究包含两条主线。第一个研究使用预测信息来达到更好的系统性能。其目的是建立一个理论框架，以评估不同类型的信息的有效性，并量化信息的准确性的影响。从随机过程的渐近分析技术将被扩展到纳入和分析预测信息的影响。此外，还将构建先进的数据信息策略，这些策略可以大幅提高性能，并且对预测误差具有鲁棒性。重点是资源有限的环境中，外部性需要仔细建模和研究。受医疗保健应用的启发，将开发面向多尺度不确定性的瞬态性能分析和优化的新范式。第二个线程研究如何利用数据来建立更好的随机模型。我们的目标是开发一个数据驱动的建模框架，提供准确的量化系统操作中的显着权衡。模型校准，因果推理和绩效评估的关键挑战将通过将计量经济学工具与随机建模相结合来解决。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

SRPT Scheduling Discipline in Many-Server Queues with Impatient Customers

具有不耐烦客户的多服务器队列中的 SRPT 调度规则

• DOI: 10.1287/mnsc.2021.4110
• 发表时间: 2021
• 期刊: Management Science
• 影响因子: 5.4
• 作者: Dong, Jing;Ibrahim, Rouba
• 通讯作者: Ibrahim, Rouba

Optimal Scheduling of Proactive Service with Customer Deterioration and Improvement

• DOI: 10.1287/mnsc.2021.3992
• 发表时间: 2021-12
• 期刊: Manag. Sci.
• 作者: Yue Hu;Carri W. Chan;Jing Dong

Use of Real-Time Information to Predict Future Arrivals in the Emergency Department

使用实时信息预测急诊室的未来到达情况

• DOI: 10.1016/j.annemergmed.2022.11.005
• 发表时间: 2023
• 期刊: Annals of Emergency Medicine
• 影响因子: 6.2
• 作者: Hu, Yue;Cato, Kenrick D.;Chan, Carri W.;Dong, Jing;Gavin, Nicholas;Rossetti, Sarah C.;Chang, Bernard P.
• 通讯作者: Chang, Bernard P.

Optimal Routing Under Demand Surges: The Value of Future Arrival Rates

需求激增下的最佳路线：未来到达率的价值

• DOI: 10.1287/opre.2022.0282
• 发表时间: 2023
• 期刊: Operations Research
• 影响因子: 2.7
• 作者: Chen, Jinsheng;Dong, Jing;Shi, Pengyi
• 通讯作者: Shi, Pengyi

Association Between Delayed Discharge From Acute Care and Rehabilitation Outcomes and Length of Stay: A Retrospective Cohort Study

急性护理延迟出院与康复结果和住院时间之间的关联：一项回顾性队列研究

• DOI: 10.1016/j.apmr.2022.05.017
• 发表时间: 2023
• 期刊: Archives of Physical Medicine and Rehabilitation
• 影响因子: 4.3
• 作者: Görgülü, Berk;Dong, Jing;Hunter, Karen;Bettio, Krista M.;Vukusic, Betty;Ranisau, Jonathan;Spencer, Gary;Tang, Terence;Sarhangian, Vahid
• 通讯作者: Sarhangian, Vahid