Sequential Decision-making for Ride-hailing Fleet Control: A Unifying Perspective

TL;DR

This paper introduces a unified decision-making framework for managing ride-hailing fleets, enabling analysis of various fleet types and operational strategies using scalable methods and real-world data-based benchmarks.

Contribution

It presents a novel, comprehensive modeling and solution approach for fleet control, including scalable assignment enumeration and benchmark creation, facilitating new insights into fleet interactions.

Findings

No significant revenue difference between internal combustion and fast-charging electric fleets.

Both fleet types outperform slow-charging electric fleets.

Pooling improves revenue and reduces variability across all fleet types.

Abstract

This paper provides a unified framework for the problem of controlling a fleet of ride-hailing vehicles under stochastic demand. We introduce a sequential decision-making model that consolidates several problem characteristics and can be easily extended to include additional characteristics. To solve the problem, we design an efficient procedure for enumerating all feasible vehicle-to-request assignments, and we introduce scalable techniques to deal with the exploration-exploitation tradeoff. We construct reusable benchmark instances that are based on real-world data and that capture a range of spatial structures and demand distributions. Our proposed modelling framework, policies and benchmark instances allow us to analyze interactions between problem characteristics that were not previously studied. We find no significant difference between revenue generated by internal combustion engine fleets and fast-charging electric fleets, but both significantly outperform slow-charging electric fleets. We also find that pooling increases the revenue, and reduces revenue variability, for all fleet types. Our contributions can help coordinate the significant research effort that this problem continues to receive.