Work

Robust heterogeneous fleet management for dual-sourcing on-demand ride services under demand uncertainty

Year: 2026

Type: article

Abstract: Motivated by the emerging dual-sourcing strategy with a blended workforce in on-demand ride service markets, this paper investigates the impact of this strategy on ride services and sizes a heterogeneous fleet operated by both contractual and freelance drivers. We present an integrated heterogeneous fleet management model that incorporates strategic fleet sizing and tactical platform operations, addressing demand uncertainty through an adaptive passenger–vehicle matching rule that outperforms conventional static methods. Employing a two-stage robust optimization framework, the second stage is reformulated into equivalent mathematical programming formulations, solvable using state-of-the-art commercial solvers. A column-and-constraint generation based exact algorithm is developed to solve the two-stage model by leveraging its structural properties. Extensive experiments based on a real-world ride-hailing service demonstrate significant benefits of the dual-sourcing strategy in enhancing service performance.

Source: Computers & Industrial Engineering

DOI: 10.1016/j.cie.2026.111880

Authors: Zhen Guo, Peng Jia, Yu Zhou, Bin Yu

Institutions: Dalian Maritime University, Beihang University

Language: English

Topic: Transportation and Mobility Innovations

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,790