Cheng Hua, Ph.D. Candidate, Fast Algorithms for Three-State Spatial Queuing Problems with Applications to Large Scale Emergency Service Systems, 3:45pm-4:45pm, Nov. 28th, 2019 (Thursday), Room N412, Shunde Building 2019.11.26

[Time] 3:45pm-4:45pm, Nov. 28th, 2019 (Thursday)
[Venue] Room N412, Shunde Building
[Speaker]  Cheng Hua, Ph.D. Candidate
[Host] Dr. Zhizhong Li
[Title]  Fast Algorithms for Three-State Spatial Queuing Problems with Applications to Large Scale Emergency Service Systems
[Abstract] We focus on modeling and evaluating a novel emergency service system in which cross-trained fire-medics respond to both fire calls and medical emergencies. Fire demand in the U.S. has decreased dramatically in the last three decades, while emergency medical calls have surged. With this changing landscape, cities are under pressure to reduce their budgets by closing fire stations. We show that a better alternative would be to implement a fire-medic system in terms of cost savings and response time performance. We develop an exact spatial queuing model and two approximation methods, the second of which has linear complexity and can be used to solve three-state problems of any size. This paper is the first to develop a fast algorithm for general three-state spatial queuing problems. In several constructed examples, performance errors are less than 1% compared to exact values. We apply our method to the fire-medic system in St. Paul, MN. and find the errors between predicted and actual average response times are less than 2%. We show, in St. Paul, the traditional system would require 33% more personnel than the fire-medic system to achieve the same mean response times. We believe the fire-medic approach and our modeling have widespread applicability to other cities.
[Short bio] Cheng Hua is currently a Ph.D. Candidate in Operations Management at the Yale School of Management under the supervision of Dr. Arthur Swersey, Dr. Tauhid Zaman, and Dr. Edward Kaplan. He also received a M.A. in Statistics from the Department of Statistics and Data Science at Yale University. Prior to joining the doctoral program at Yale, he obtained both bachelor's degrees in Electrical & Computer Engineering from Shanghai Jiaotong University, and Industrial & Operations Engineering, with a minor in Mathematics, from the University of Michigan.His research interests include devising and applying novel and fast algorithms in real world problems, such as service operations and public-sector operations. He is also interested in applying deep learning and neural networks to classic stochastic systems. His other research interests lie in the fields of large-scale data driven and machine learning approaches in financial market and sports analytics.He has won many awards from INFORMS, IIE, and affiliated universities. He also has experiences in teaching MBA and EMBA students at Yale University.

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