Journal article
Optimizing large on-demand transportation systems through stochastic conic programming
European journal of operational research, Vol.295(2), pp.427-442
12/01/2021
DOI: 10.1016/j.ejor.2020.10.053
Abstract
•Develop a generic and scalable stochastic conic program for the On-demand transportation system OTS vehicle repositioning problem with endogenous demand. The controller makes a joint decision of fleet operations and demand controls.•This work provides a probabilistic guarantee for satisfying service level constraints constraints with a finite number of vehicles. This work demonstrates how to solve stochastic fleet optimization in large-scale OTS by a second-order conic (SOC) program, or a convex program in its extensions.
On-demand transportation systems (OTS) are increasingly popular worldwide. Prior literature has studied how to control vehicle fleet in queueing-networks to rebalance excess supply or demand in OTS. This aggregated setting models the stochastic demand process and decompose large-scale networks for which product-form equilibrium distributions exist. However, such an approach is unsatisfactory in terms of computational complexity for its dependence on vehicle numbers. This paper presents a stochastic conic programming approach that obtains the near-optimal vehicle repositioning controls with endogenous demand with mild computational complexity and high fidelity. This global framework covers most existing queueing-network-based OTS models in the literature. Leveraging this approach, we explore day-to-day vehicle repositioning problems for on-demand vehicle operations in New York City. These results support the potential for providing a more accessible and sustainable on-demand mobility service, which is of particular significance as multimodal transport continues to emerge.
Details
- Title: Subtitle
- Optimizing large on-demand transportation systems through stochastic conic programming
- Creators
- Shukai Li - Northwestern UniversityQi Luo - Cornell UniversityRobert Cornelius Hampshire - Ford Motor Company
- Resource Type
- Journal article
- Publication Details
- European journal of operational research, Vol.295(2), pp.427-442
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.ejor.2020.10.053
- ISSN
- 0377-2217
- eISSN
- 1872-6860
- Language
- English
- Date published
- 12/01/2021
- Academic Unit
- Business Analytics
- Record Identifier
- 9984696724302771
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