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Modelling and Optimization for the Employment of Full-Time and Part-Time Drivers in Ride-Hailing Platforms

Received: 18 October 2021     Accepted: 5 November 2021     Published: 12 November 2021
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Abstract

The employment strategy is of great significance to improve the performances of the ride-hailing platforms. This paper establishes a nonlinear programming model to characterize the employment problem of a ride-hailing platform where full-time drivers and part-time drivers coexist, facing a month-varied daily market size. The daily online time of the full-time drivers is assumed to be fixed, while the daily online time of the part-time drivers is self-scheduled. The objective of the decision model is to maximize the total effective demand of the ride-hailing platform, and the constraints are to guarantee the minimum income of both the full-time and part-time drivers. By solving the models, we find the decision problem can be simplified as a linear programming model and the optimal numbers of full-time drivers and part-time drivers are derived. Theoretical results show that it is optimal for the ride-hailing platform to employ only part-time drivers if the cap of number of part-time drivers is sufficiently high, and the number of part-time drivers satisfies that the expected income per unit time during the month with the minimum daily market size equals the required minimum income. Otherwise, if the cap of number of part-time drivers is not sufficiently high, full-time drivers and part-time drivers will coexist. Finally, a numerical experiment is conducted to illustrate the theoretical results.

Published in International Journal of Economics, Finance and Management Sciences (Volume 9, Issue 6)
DOI 10.11648/j.ijefm.20210906.11
Page(s) 221-224
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Optimization, Employment, Ride-Hailing Platforms

References
[1] New economy, new employment - 2017 didi travel platform Employment Research Report [R]. Didi Policy Research Institute. 2017. http://www.shujuju.cn/lecture/detail/3532.
[2] Hall JV, Krueger AB. An analysis of the labor market for Uber’s driver-partners in the United States [J]. NBER Working paper, Cambridge, MA. 2016.
[3] Benjaafar S., Ding J Y, Kong G., et al. Labor Welfare in On-Demand Service Platforms [J]. Social Science Electronic Publishing. 2018.
[4] Gurvich I., Lariviere M., Moreno A. Operations in the on-demand economy: staffing services with self-scheduling capacity [J]. Social Science Electronic Publishing. 2016.
[5] Zhong Y., Lin Z. Matching supply and demand on ride-sharing platforms with permanent agents and competition [J]. International Journal of Production Economics. 2019, 218: 363-374.
[6] Ibrahim R. Managing queueing systems where capacity is random and customers are impatient [J]. Production and Operations Management. 2018, 27 (2): 234–250.
[7] Taylor T. A. On-Demand Service Platforms [J]. Manufacturing & Service Operations Management. 2018, 20 (4): 704-720.
[8] Bai J., So K., Tang C. S., et al. Coordinating supply and demand on an on-demand service platform with impatient customers [J]. Manufacturing & Service Operations Management. 2019. 21 (3): 556-570.
[9] Chen M. K., Chevalier J. A., Rossi P. E., et al. The Value of Flexible work: Evidence from Uber drivers [J]. National Bureau of Economic Research. 2017.
[10] Farber H. S. Is tomorrow another day? The labor supply of New York city cabdrivers [J]. Journal of political Economy. 2005, 113 (1): 46–82.
[11] Farber H. S. Why you can’t find a taxi in the rain and other labor supply lessons from cab drivers [J]. The Quarterly Journal of Economics. 2015, 130 (4): 1975–2026.
[12] Chen M. K., Sheldon M. Dynamic pricing in a labor market: Surge pricing and flexible work on the Uber platform [J]. Proceedings of the 2016 ACM Conference on Economics and Computation. ACM. 2016, 455.
[13] David P. Baron. Disruptive Entrepreneurship and Dual Purpose Strategies: The Case of Uber [J]. Strategy Science. 2019, 3 (2): 439-462.
[14] Yu J. J, Tang C. S., Shen Z., et al. A balancing act of regulating on-demand ride services [J]. Management Science. 2020, 66 (7): 2975-2992.
Cite This Article
  • APA Style

    Chunhui Liu, Yangguang Lu. (2021). Modelling and Optimization for the Employment of Full-Time and Part-Time Drivers in Ride-Hailing Platforms. International Journal of Economics, Finance and Management Sciences, 9(6), 221-224. https://doi.org/10.11648/j.ijefm.20210906.11

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    ACS Style

    Chunhui Liu; Yangguang Lu. Modelling and Optimization for the Employment of Full-Time and Part-Time Drivers in Ride-Hailing Platforms. Int. J. Econ. Finance Manag. Sci. 2021, 9(6), 221-224. doi: 10.11648/j.ijefm.20210906.11

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    AMA Style

    Chunhui Liu, Yangguang Lu. Modelling and Optimization for the Employment of Full-Time and Part-Time Drivers in Ride-Hailing Platforms. Int J Econ Finance Manag Sci. 2021;9(6):221-224. doi: 10.11648/j.ijefm.20210906.11

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  • @article{10.11648/j.ijefm.20210906.11,
      author = {Chunhui Liu and Yangguang Lu},
      title = {Modelling and Optimization for the Employment of Full-Time and Part-Time Drivers in Ride-Hailing Platforms},
      journal = {International Journal of Economics, Finance and Management Sciences},
      volume = {9},
      number = {6},
      pages = {221-224},
      doi = {10.11648/j.ijefm.20210906.11},
      url = {https://doi.org/10.11648/j.ijefm.20210906.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20210906.11},
      abstract = {The employment strategy is of great significance to improve the performances of the ride-hailing platforms. This paper establishes a nonlinear programming model to characterize the employment problem of a ride-hailing platform where full-time drivers and part-time drivers coexist, facing a month-varied daily market size. The daily online time of the full-time drivers is assumed to be fixed, while the daily online time of the part-time drivers is self-scheduled. The objective of the decision model is to maximize the total effective demand of the ride-hailing platform, and the constraints are to guarantee the minimum income of both the full-time and part-time drivers. By solving the models, we find the decision problem can be simplified as a linear programming model and the optimal numbers of full-time drivers and part-time drivers are derived. Theoretical results show that it is optimal for the ride-hailing platform to employ only part-time drivers if the cap of number of part-time drivers is sufficiently high, and the number of part-time drivers satisfies that the expected income per unit time during the month with the minimum daily market size equals the required minimum income. Otherwise, if the cap of number of part-time drivers is not sufficiently high, full-time drivers and part-time drivers will coexist. Finally, a numerical experiment is conducted to illustrate the theoretical results.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Modelling and Optimization for the Employment of Full-Time and Part-Time Drivers in Ride-Hailing Platforms
    AU  - Chunhui Liu
    AU  - Yangguang Lu
    Y1  - 2021/11/12
    PY  - 2021
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    DO  - 10.11648/j.ijefm.20210906.11
    T2  - International Journal of Economics, Finance and Management Sciences
    JF  - International Journal of Economics, Finance and Management Sciences
    JO  - International Journal of Economics, Finance and Management Sciences
    SP  - 221
    EP  - 224
    PB  - Science Publishing Group
    SN  - 2326-9561
    UR  - https://doi.org/10.11648/j.ijefm.20210906.11
    AB  - The employment strategy is of great significance to improve the performances of the ride-hailing platforms. This paper establishes a nonlinear programming model to characterize the employment problem of a ride-hailing platform where full-time drivers and part-time drivers coexist, facing a month-varied daily market size. The daily online time of the full-time drivers is assumed to be fixed, while the daily online time of the part-time drivers is self-scheduled. The objective of the decision model is to maximize the total effective demand of the ride-hailing platform, and the constraints are to guarantee the minimum income of both the full-time and part-time drivers. By solving the models, we find the decision problem can be simplified as a linear programming model and the optimal numbers of full-time drivers and part-time drivers are derived. Theoretical results show that it is optimal for the ride-hailing platform to employ only part-time drivers if the cap of number of part-time drivers is sufficiently high, and the number of part-time drivers satisfies that the expected income per unit time during the month with the minimum daily market size equals the required minimum income. Otherwise, if the cap of number of part-time drivers is not sufficiently high, full-time drivers and part-time drivers will coexist. Finally, a numerical experiment is conducted to illustrate the theoretical results.
    VL  - 9
    IS  - 6
    ER  - 

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Author Information
  • School of Business Administration, South China University of Technology, Guangzhou, China

  • Big Data Institute, Sany Heavy Machinery Ltd., Suzhou, China

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