IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i14p2290-d1440333.html
   My bibliography  Save this article

The Application of the Piecewise Linear Method for Non-Linear Programming Problems in Ride-Hailing Assignment Based on Service Level, Driver Workload, and Fuel Consumption

Author

Listed:
  • Tubagus Robbi Megantara

    (Doctoral Program in Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Sudradjat Supian

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Diah Chaerani

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Abdul Talib Bon

    (Department of Production and Operations, Universiti Tun Hussein Onn Malaysia, Johor 86400, Malaysia)

Abstract

Ride-hailing services have grown rapidly, presenting challenges such as increased traffic congestion, inefficient driver workload distribution, and environmental concerns like higher fuel consumption and emissions. This study develops a non-linear ride-hailing assignment model addressing these issues by considering service level, driver workload, and fuel consumption. A piecewise linear method was employed to handle a non-linear programming model, and the method was modified to function autonomously without operator intervention. The model’s performance was evaluated using a publicly accessible dataset of taxi trips in Manhattan, focusing on indicators such as passenger waiting time, driver workload distribution, and fuel consumption. Numerical simulations demonstrated significant improvements: a 15% reduction in average passenger waiting time, a 20% improvement in balancing driver workloads, and a 10% decrease in overall fuel consumption, contributing to reduced emissions and environmental impact. The modified piecewise linear method proved effective in optimizing ride-hailing assignments, providing a more efficient and sustainable solution. The model also showed robustness in handling large datasets, ensuring scalability and applicability to various urban settings. These findings highlight the model’s potential to enhance operational efficiency and promote sustainability in ride-hailing services. By integrating considerations for service level, driver workload, and fuel consumption, the model offers a holistic approach to addressing the key challenges faced by the ride-hailing industry. This study provides valuable insights for future ride-hailing development and implementations of ride-hailing systems, promoting practices that are both efficient and environmentally friendly.

Suggested Citation

  • Tubagus Robbi Megantara & Sudradjat Supian & Diah Chaerani & Abdul Talib Bon, 2024. "The Application of the Piecewise Linear Method for Non-Linear Programming Problems in Ride-Hailing Assignment Based on Service Level, Driver Workload, and Fuel Consumption," Mathematics, MDPI, vol. 12(14), pages 1-23, July.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2290-:d:1440333
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/14/2290/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/14/2290/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dimitris Bertsimas & Patrick Jaillet, & Sébastien Martin, 2019. "Online Vehicle Routing: The Edge of Optimization in Large-Scale Applications," Operations Research, INFORMS, vol. 67(1), pages 143-162, January.
    2. Guo, Xiaotong & Caros, Nicholas S. & Zhao, Jinhua, 2021. "Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 161-189.
    3. Yang, Hai & Qin, Xiaoran & Ke, Jintao & Ye, Jieping, 2020. "Optimizing matching time interval and matching radius in on-demand ride-sourcing markets," Transportation Research Part B: Methodological, Elsevier, vol. 131(C), pages 84-105.
    4. Agatz, Niels A.H. & Erera, Alan L. & Savelsbergh, Martin W.P. & Wang, Xing, 2011. "Dynamic ride-sharing: A simulation study in metro Atlanta," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1450-1464.
    5. Loay Alkhalifa & Hans Mittelmann, 2022. "New Algorithm to Solve Mixed Integer Quadratically Constrained Quadratic Programming Problems Using Piecewise Linear Approximation," Mathematics, MDPI, vol. 10(2), pages 1-15, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tubagus Robbi Megantara & Sudradjat Supian & Diah Chaerani, 2022. "Strategies to Reduce Ride-Hailing Fuel Consumption Caused by Pick-Up Trips: A Mathematical Model under Uncertainty," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    2. Daganzo, Carlos F. & Ouyang, Yanfeng & Yang, Haolin, 2020. "Analysis of ride-sharing with service time and detour guarantees," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 130-150.
    3. Rajendran, Suchithra & Srinivas, Sharan & Grimshaw, Trenton, 2021. "Predicting demand for air taxi urban aviation services using machine learning algorithms," Journal of Air Transport Management, Elsevier, vol. 92(C).
    4. Peng, Zixuan & Shan, Wenxuan & Zhu, Xiaoning & Yu, Bin, 2022. "Many-to-one stable matching for taxi-sharing service with selfish players," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 255-279.
    5. Stumpe, Miriam & Dieter, Peter & Schryen, Guido & Müller, Oliver & Beverungen, Daniel, 2024. "Designing taxi ridesharing systems with shared pick-up and drop-off locations: Insights from a computational study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    6. Ke, Jintao & Yang, Hai & Zheng, Zhengfei, 2020. "On ride-pooling and traffic congestion," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 213-231.
    7. Ke, Jintao & Yang, Hai & Li, Xinwei & Wang, Hai & Ye, Jieping, 2020. "Pricing and equilibrium in on-demand ride-pooling markets," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 411-431.
    8. Rui Yao & Shlomo Bekhor, 2021. "A Dynamic Tree Algorithm for Peer-to-Peer Ridesharing Matching," Networks and Spatial Economics, Springer, vol. 21(4), pages 801-837, December.
    9. Si, Jinhua & He, Fang & Lin, Xi & Tang, Xindi, 2024. "Vehicle dispatching and routing of on-demand intercity ride-pooling services: A multi-agent hierarchical reinforcement learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    10. Zhen, Lu & Baldacci, Roberto & Tan, Zheyi & Wang, Shuaian & Lyu, Junyan, 2022. "Scheduling heterogeneous delivery tasks on a mixed logistics platform," European Journal of Operational Research, Elsevier, vol. 298(2), pages 680-698.
    11. Sudradjat Supian & Subiyanto & Tubagus Robbi Megantara & Abdul Talib Bon, 2024. "Ride-Hailing Matching with Uncertain Travel Time: A Novel Interval-Valued Fuzzy Multi-Objective Linear Programming Approach," Mathematics, MDPI, vol. 12(9), pages 1-17, April.
    12. Meng Li & Guowei Hua & Haijun Huang, 2018. "A Multi-Modal Route Choice Model with Ridesharing and Public Transit," Sustainability, MDPI, vol. 10(11), pages 1-14, November.
    13. Tafreshian, Amirmahdi & Masoud, Neda, 2022. "A truthful subsidy scheme for a peer-to-peer ridesharing market with incomplete information," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 130-161.
    14. Sumitkumar, Rathor & Al-Sumaiti, Ameena Saad, 2024. "Shared autonomous electric vehicle: Towards social economy of energy and mobility from power-transportation nexus perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    15. Xingyuan Li & Jing Bai, 2021. "A Ridesharing Choice Behavioral Equilibrium Model with Users of Heterogeneous Values of Time," IJERPH, MDPI, vol. 18(3), pages 1-22, January.
    16. Chen, Shijie & Rahman, Md Hishamur & Marković, Nikola & Siddiqui, Muhammad Imran Younus & Mohebbi, Matthew & Sun, Yanshuo, 2024. "Schedule negotiation with ADA paratransit riders under value of time uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 184(C).
    17. Gurumurthy, Krishna Murthy & Kockelman, Kara M., 2021. "Impacts of shared automated vehicles on airport access and operations, with opportunities for revenue recovery: Case Study of Austin, Texas," Research in Transportation Economics, Elsevier, vol. 90(C).
    18. Dessouky, Maged M & Hu, Shichun, 2021. "Dynamic Routing for Ride-Sharing," Institute of Transportation Studies, Working Paper Series qt6qq8r7hz, Institute of Transportation Studies, UC Davis.
    19. Yue Guo & Fu Xin & Xiaotong Li, 2020. "The market impacts of sharing economy entrants: evidence from USA and China," Electronic Commerce Research, Springer, vol. 20(3), pages 629-649, September.
    20. Wang, Tao & Guo, Jia & Zhang, Wei & Wang, Kai & Qu, Xiaobo, 2024. "On the planning of zone-based electric on-demand minibus," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2290-:d:1440333. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.