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Workforce planning for O2O delivery systems with crowdsourced drivers

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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.

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References

  • Archetti, C., Speranza, M., & Savelsbergh, M. (2016). The vehicle routing problem with occasional drivers. European Journal of Operational Research,254(2), 472–480.

    Google Scholar 

  • 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.

  • Azi, N., Gendreau, M., & Potvin, J. Y. (2012). A dynamic vehicle routing problem with multiple delivery routes. Annals of Operations Research,199(1), 103–112.

    Google Scholar 

  • 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.

  • Cachon, G. P., Daniels, K. M., & Lobel, R. (2017). The role of surge pricing on a service platform with self-scheduling capacity. Manufacturing & Service Operations Management,19(3), 368–384.

    Google Scholar 

  • Chen, W., Song, J., Shi, L., Pi, L., & Sun, P. (2013). Data mining-based dispatching system for solving the local pickup and delivery problem. Annals of Operations Research,203(1), 351–370.

    Google Scholar 

  • Devari, A., Nikolaev, A. G., & He, Q. (2017). Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers. Transportation Research Part E,105, 105–122.

    Google Scholar 

  • Du, Y., & Hall, R. (1997). Fleet sizing and empty equipment redistribution for center terminal transportation networks. Management Science,43, 145–157.

    Google Scholar 

  • Ge, J. (2017). Traditional retail distribution in megacities. Doctoral dissertation, Technische Universiteit Eindhoven.

  • 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.

  • He, Y., Zhang, J., Gou, Q., & Bi, G. (2018). Supply chain decisions with reference quality effect under the O2O environment. Annals of Operations Research,268(1–2), 273–292.

    Google Scholar 

  • Kafle, N., Zou, B., & Lin, J. (2017). Design and modeling of a crowdsource-enabled system for urban parcel relay and delivery. Transportation Research Part B,99, 62–82.

    Google Scholar 

  • Klapp Belmar, M. A. (2016). Dynamic optimization for same-day delivery operations. Doctoral dissertation, Georgia Institute of Technology.

  • Lee, S., Kang, Y., & Prabhu, V. V. (2016). Smart logistics: Distributed control of green crowdsourced parcel services. International Journal of Production Research,54(23), 6956–6968.

    Google Scholar 

  • Love, R., & Morris, J. (1979). Mathematical models of road travel distances. Management Science,25(2), 130–139.

    Google Scholar 

  • Miller, J., Nie, Y., & Stathopoulos, A. (2017). Crowdsourced urban package delivery: Modeling traveler willingness to work as crowdshippers. Transportation Research Record,2610(1), 67–75.

    Google Scholar 

  • Punel, A., & Stathopoulos, A. (2017). Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects. Transportation Research Part E,105, 18–38.

    Google Scholar 

  • Qureshi, M., Hwang, H. L., & Chin, S. M. (2002). Comparison of distance estimates for commodity flow survey: Great circle distances versus network-based distances. Transportation Research Record: Journal of the Transportation Research Board,1804, 212–216.

    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.

  • Smilowitz, K., Nowak, M., & Jiang, T. (2013). Workforce management in periodic delivery operations. Transportation Science,47(2), 214–230.

    Google Scholar 

  • Talyor, T. (2017). On-demand service platforms. Manufacturing & Service Operations Management,20(4), 704–720.

    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.

  • Yu, Y., Han, X., Liu, J., & Cheng, Q. (2015). Supply chain equilibrium among companies with offline and online selling channels. International Journal of Production Research,53(22), 6672–6688.

    Google Scholar 

  • Zha, L., Yin, Y., & Yang, H. (2016). Economic analysis of ride-sourcing markets. Transportation Research Part C,71, 249–266.

    Google Scholar 

  • Zhao, F., Wu, D., Liang, L., & Dolgui, A. (2016). Lateral inventory transshipment problem in online-to-offline supply chain. International Journal of Production Research,54(7), 1951–1963.

    Google Scholar 

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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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Authors and Affiliations

  1. Business School, Central University of Finance and Economics, Beijing, 100081, People’s Republic of China

    Hongyan Dai

  2. Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore, 117576, Singapore

    Peng Liu

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  1. Hongyan Dai
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  2. Peng Liu
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Correspondence to Peng Liu.

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Dai, H., Liu, P. Workforce planning for O2O delivery systems with crowdsourced drivers. Ann Oper Res 291, 219–245 (2020). https://doi.org/10.1007/s10479-019-03135-z

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