Value of Dispatching Flexibility in On-Demand Delivery

On-demand delivery has emerged as a fast-growing market, as customers crave speed for their purchases. Through an analysis of Meituan’s data, we find that a service region is often divided into delivery zones, and couriers may or may not have the flexibility to serve demand across zones. Motivated by this observation, this paper investigates whether providing dispatching flexibility to couriers is beneficial and, if so, how to improve delivery efficiency by tailoring the flexibility design to the market. We model the on-demand delivery system with and without batching using queueing approximations. The expected customer wait time is used to measure the performance of different systems. We find that the fully flexible delivery system yields the best performance for single-order delivery. However, full flexibility may not be beneficial for batch delivery. We prove that the dedicated delivery system outperforms the fully flexible delivery system when every courier delivers two orders in high-traffic regimes. Furthermore, we propose a partially flexible delivery system in which only a fraction of couriers are flexible enough to serve multiple zones, and we show that it can perform better than both dedicated and fully flexible delivery systems. We perform simulations with synthetic data and a case study using real-world data from Meituan, both of which corroborate our theoretical findings. Our study reveals the advantages and limitations of dispatching flexibility in on-demand delivery. Pooling couriers to serve all zones enhances system responsiveness and adaptability to order arrival uncertainty, but carries the risk of inducing increased delivery distances and slower service processes with batch delivery. Interestingly, the proposed partially flexible delivery system demonstrates significant potential to improve operational efficiencies while reducing the potential hassle of operating a fully flexible system.

History: This paper has been accepted for the Transportation Science Special Issue, “The First INFORMS TSL Data-Driven Research Challenge.”

Funding: H. Wang acknowledges the support from the Start-up Fund for RAPs under the Strategic Hiring Scheme at PolyU [Grant P0058278]. S. Liu acknowledges the support from the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2022-04950]. S. Wang acknowledges the support from the National Natural Science Foundation of China [Grant 72371221], and the Research Grants Council of the Hong Kong Special Administrative Region, China [Project HKSAR RGC TRS T32-707/22-N].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2025.0095.