O2O on-demand delivery optimization with mixed driver forces

Hongyan Dai, Jiawei Tao, Hai Jiang, Weiwei Chen

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


O2O (Online to Offline) service enables customers to place orders online and receive products/services offline. In addition to traditional in-house drivers, the emergence of crowd-sourced drivers provides an opportunity to re-organize the offline delivery. In practice, three types of workforce, namely, in-house drivers, full-time and part-time crowd-sourced drivers, coexist in the system with different characteristics. This posts challenges to the management of order assignment and routing by the online platform. In particular,we study the impact of the detour flexibility for part-time crowd-sourced drivers, which affects the participation rate of this type of workforce. This paper aims to provide a systematic method for the O2O platforms to optimize order assignment and routing. We further validate our model and study the managerial insights using real datasets from one of the mainstream O2O platforms in China.

Original languageEnglish (US)
Pages (from-to)391-396
Number of pages6
Issue number13
StatePublished - Sep 2019
Event9th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2019 - Berlin, Germany
Duration: Aug 28 2019Aug 30 2019

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


  • Crowd-sourcing
  • Logistics
  • O2O
  • Order assignment

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