Jiheng Zhang is the head and a professor in the Department of Industrial Engineering and Decision Analytics of HKUST. He also hold a joint appointment at the Department of Mathematics of HKUST. His research interests are in the areas of Stochastic Modeling and Optimization, Statistical Learning, Numerical Methods and Algorithms; with applications in Operations Management, Large Communication Networks, and Financial Technology. He serves as an associate editor for Operations Research, Stochastic Systems, Probability in the Engineering and Informational Sciences. He has been the co-director of Elliptic lab since 2018, focusing on various practical projects with industry partners including Huawei and Webank. He has invented several patents on large-scale production planning and blockchain consensus mechanism design with industry partners. He received his Ph.D. degree in operations research from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology in 2009. He also holds an M.S. in mathematics from Ohio State University and a B.S. in mathematics from Nanjing University.
| Venue | Seminar room at i4.0 building (Level 1) |
| Link to Register
(Hybrid session) |
https://nus-sg.zoom.us/meeting/register/tZAlc-2uqz4vGddG2dIGCNr8NaOpCl8Wp_Xw |
| Title | On-Demand Ride-Matching in a Spatial Model with Abandonment and Cancellation |
| Abstract | Ride-hailing platforms such as Uber, Lyft, and DiDi coordinate supply and demand by matching passen- gers and drivers. The platform has to promptly dispatch drivers when receiving requests, since otherwise passengers may lose patience and abandon the service by switching to alternative transportation methods. However, having less idle drivers results in a possible lengthy pick-up time, which is a waste of system capacity and may cause passengers to cancel the service after they are matched. Due to complex spatial and queueing dynamics, the analysis of the matching decision is challenging. In this paper, we propose a spatial model to approximate the pick-up time based on the number of waiting passengers and idle drivers. We analyze the dynamics of passengers and drivers in a queueing model where the platform can control the matching process by setting a threshold on the expected pick-up time. Applying fluid approximations, we obtain accurate performance evaluations and an elegant optimality condition, based on which we propose a policy that adapts to time-varying demand. |
