J
J. G. Dai
Researcher at Cornell University
Publications - 19
Citations - 788
J. G. Dai is an academic researcher from Cornell University. The author has contributed to research in topics: Queueing theory & Stationary distribution. The author has an hindex of 12, co-authored 17 publications receiving 591 citations. Previous affiliations of J. G. Dai include The Chinese University of Hong Kong & Georgia Institute of Technology.
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Empty-Car Routing in Ridesharing Systems
TL;DR: It is proved that the optimal network utility obtained from the fluid-based optimization is an upper bound on the utility in the finite car system for any routing policy, both static and dynamic, under which the closed queueing network has a stationary distribution.
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Models and Insights for Hospital Inpatient Operations: Time-Dependent ED Boarding Time
TL;DR: The model predicts that implementing a hypothetical policy can eliminate excessive waiting for those patients who request beds in mornings, and is able to capture the inpatient flow dynamics at hourly resolution and can evaluate the impact of operational policies on both the daily and time-of-day waiting time performance.
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Empty-car routing in ridesharing systems
TL;DR: In this article, the authors considered a closed queueing network model of ridesharing systems such as Didi Chuxing, Lyft, and Uber, and established both process-level and steady-state convergence of the queuing network to a fluid limit and used this limit to study a fluid-based optimization problem.
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Stein’s method for steady-state diffusion approximations: An introduction through the Erlang-A and Erlang-C models
TL;DR: In this article, the authors introduced the Stein method framework in the context of steady-state diffusion approximations and proved that the distance between the stationary distribution of a normalized customer count process and that of an appropriately defined diffusion process decrease at a rate of 1/R, where R is the offered load.
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Stein's method for steady-state diffusion approximations of $M/Ph/n+M$ systems
Anton Braverman,J. G. Dai +1 more
TL;DR: In this article, it was shown that the Wasserstein distance between the stationary distribution of the normalized system size process and that of a piecewise Ornstein-Uhlenbeck (OU) process is bounded by a constant constant, where the constant is independent of the arrival rate and the number of servers.