Optimizing large on-demand transportation systems through stochastic conic programming

Shukai Li, Qi Luo and Robert Cornelius Hampshire

European Journal of Operational Research, 2021, vol. 295, issue 2, 427-442

Abstract: 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.

Keywords: Production; Manufacturing; Transportation and logistics; On-demand transportation systems; Queueing networks; Stochastic conic program (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:295:y:2021:i:2:p:427-442

DOI: 10.1016/j.ejor.2020.10.053

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