Optimizing Large On-demand Transportation Systems through Stochastic Conic Programming

Abstract

On-demand transportation systems (OTS) are increasingly popular worldwide. Modeling these systems as closed queueing networks (CQN) or semi-open queueing networks (SOQN) makes optimizations tractable, and controls robust to input data. Prior literature has obtained mean performance measures (e.g., vehicle availability) by decomposing OTS into networks for which product-form equilibrium distributions exist. However, such an approach is unsatisfactory in terms of computational complexity for large networks such as micromobility networks. This paper presents an alternative approach to obtaining mean performance measures with mild computational complexity and high fidelity. Leveraging this approach, we explore day-to-day vehicle redistribution and network pricing problems for OTS operations in New York City. These results support the potential for rebuilding a more accessible and sustainable OTS, which is of particular significance as multimodal transport continues to emerge globally.

Keywords: Production, Manufacturing, Transportation and Logistics, On-demand Transportation Systems, Queueing Networks, Vehicle Rebalancing