Saif Benjaafar is McKnight Presidential Endowed Professor and Distinguished McKnight University Professor at the University of Minnesota. He is Head of the Department of Industrial & Systems Engineering at the University of Minnesota, where he also directs the Initiative on the Sharing Economy. He is a founding member of the Singapore University of Technology and Design where he served as Head of Engineering Systems and Design. He is the Editor in Chief of the INFORMS journal Service Science. He serves on the board of directors of Hourcar, a social car sharing organization. His research is in the area of operations broadly defined, with a current focus on sustainable operations and innovation in business models, including sharing economy, on-demand services, and digital marketplaces. He is a Fellow of INFORMS and IISE.
| Venue | BIZ 1 – 0304 |
| Link to Register
(Hybrid session) |
https://nus-sg.zoom.us/meeting/register/tZEqdumsqTIqHdynlcuEWYlO-g6cmUD5yg1t |
| Title | Dimensioning and Pricing of Shared Vehicle Networks |
| Abstract | In the first part of the talk, we consider the problem of optimal fleet sizing (dimensioning) in an on-demand and one way vehicle sharing system. We leverage a property of closed queueing networks that relates the dynamics of a network with K items to one with K-1 items to obtain explicit and closed form lower and upper bounds on the optimal number of vehicles that are asymptotically exact. We use the bounds to show that buffer capacity (capacity in excess of the nominal load) can be expressed in terms of three explicit terms that can be interpreted as follows: (1) standard buffer capacity that is protection against randomness in demand and rental times, (2) buffer capacity that is protection against vehicle roaming, and (3) a correction term. We show that the capacity needed to buffer against vehicle roaming can be substantial even in systems with vanishingly small demand. In the second part of the talk, give a fixed fleet size, we consider the dynamic pricing in such a network and show that a static pricing policy that arises from solving a maximum flow relaxation of the problem guarantees a performance ratio of order 1- O(1/) where K is the number of vehicles. The approach used, which leverages the same property of closed queueing networks used in the dimensioning problem, is startingly simple and yields performance guarantees that are tighter than those previously obtained in the literature. Time permitting, we will also discuss ongoing work that considers dimensioning and pricing in other settings with spatial queueing network features.
(The talk will draw on material from the following two papers: https://pubsonline.informs.org/doi/epdf/10.1287/mnsc.2021.3957, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3998297 and https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4130757.
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