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Workforce planning for O2O delivery systems with crowdsourced drivers

  • Hongyan Dai
  • Peng Liu
S.I.: RealCaseOR
  • 45 Downloads

Abstract

This paper proposes a workforce capacity planning model for online-to-offline (O2O) logistics systems. Three types of workforces with different compensation schemes are considered: in-house drivers, full-time crowdsourced drivers, and part-time crowdsourced drivers. We propose a cost minimization problem to determine the optimal workforce capacity and optimal order allocations, considering the dynamics of incoming demand. We apply a dataset from an O2O platform and our analysis reveals that (1) the capacity plan priority is part-time crowdsourced drivers, followed by full-time crowdsourced drivers and in-house drivers; the order assignment priority is reverse; (2) setting a proper guaranteed minimum order level and using the single service mode for full-time crowdsourced drivers can significantly reduce the rate of unfulfilled orders and total cost; and (3) leveraging the flexibility of the part-time crowdsourced drivers can significantly reduce the unfulfilled orders and total cost. Moreover, customizing the design of these schemes further enhances their potential. We expect these results to shed light on cost control and provide a model for crowd-sourcing which can improve the efficiency of O2O on-demand businesses.

Keywords

O2O In-house drivers Crowdsourced drivers Workforce planning Order assignment 

Notes

Acknowledgements

We would like to extend our sincere gratitude to Liu Yang (Assistant Professor from National University of Singapore), for her instructive advice and useful suggestions on this study. We are deeply grateful of her help in the completion of this manuscript. In addition, this study was supported by the National Natural Science Foundation of China (91646125), Beijing Natural Science Foundation (9172017), National Natural Science Foundation of China (71872200), and Singapore Ministry of Education Academic Research Fund Tier 1 (WBS No. R-266-000-084-133).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Business SchoolCentral University of Finance and EconomicsBeijingPeople’s Republic of China
  2. 2.Department of Industrial Systems Engineering and ManagementNational University of SingaporeSingaporeSingapore

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