Workforce planning for O2O delivery systems with crowdsourced drivers
- 45 Downloads
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.
KeywordsO2O In-house drivers Crowdsourced drivers Workforce planning Order assignment
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).
- Arslan, A. M., Agatz, N., Kroon, L., & Zuidwijk, R. (2018). Crowdsourced delivery—A dynamic pickup and delivery problem with ad hoc drivers. Transportation Science. https://doi.org/10.1287/trsc.2017.0803.
- Ballare, S., & Lin, J. (2018). Preliminary investigation of a crowdsourced package delivery system: A case study. In E. Taniguchi & E. Thompson (Eds.), City logistics 3: Towards sustainable and liveable cities (pp. 109–128). Wiley Online Library. https://doi.org/10.1002/9781119425472.ch6.
- Ge, J. (2017). Traditional retail distribution in megacities. Doctoral dissertation, Technische Universiteit Eindhoven.Google Scholar
- Gurvich, I., Lariviere, M., & Moreno, A. (2016). Operations in the on-demand economy: Staffing services with self-scheduling capacity. Working paper. http://ssrn.com/abstract=2336514. Accessed 28 June 2016.
- Klapp Belmar, M. A. (2016). Dynamic optimization for same-day delivery operations. Doctoral dissertation, Georgia Institute of Technology.Google Scholar
- Reyes, D., Erera, A., Savelsbergh, M., Sahasrabudhe, S., & O’Neil, R. (2018). The meal delivery routing problem. Optimization Online. Available online: http://www.optimization-online.org/DB_FILE/2018/04/6571.pdf.
- Riquelme, C., Banerjee, S., & Johari, R. (2015). Pricing in ride-share platforms: A queueing-theoretic approach. Working paper, Cornell University.Google Scholar
- Yildiz, B., & Savelsbergh, M. (2018). Service and capacity planning in crowdsourced delivery. Available at SSRN: http://dx.doi.org/10.2139/ssrn.3135550. Accessed 19 Dec 2018.