Ride-Hailing Assignment Problem under Waiting Time Uncertainty using Interval-Valued Fuzzy Quadratic


  • Sudradjat Supian Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jatinangor, Sumedang, 45363 Jawa Barat, Indonesia
  • Subiyanto Subiyanto Department of Marine Science, Faculty of Fishery and Marine Science, Universitas Padjadjaran, Jatinangor, Sumedang, 45363 Jawa Barat, Indonesia
  • Tubagus Robbi Megantara Doctoral Program of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jatinangor, Sumedang, 45363 Jawa Barat, Indonesia
  • Abdul Talib Bon Department of Production and Operations, Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia




Fuzzy Quadratic Programming, Interval-Valued Fuzzy, Ride-Hailing, Uncertainty


Ride-hailing is a creative idea created by transportation supported by science and technology. Ride-hailing services can help daily community activities. The issue with ride-hailing is that traffic conditions are unpredictable, implying that waiting times are uncertain. The time passengers spend waiting from when they book a ride service until the driver arrives at the pick-up location is called waiting time. This study suggests a quadratic programming technique for minimizing waiting time while accounting for the unpredictability of pick-up travel time. The interval-valued fuzzy quadratic programming method handles the uncertainty and imprecision of the anticipated journey time. When allocating drivers to pick up passengers, interval-valued fuzzy numbers can provide a more realistic representation of waiting time uncertainty. As a result, the interval-valued fuzzy quadratic programming model can handle the uncertainty in waiting time for ride-hailing assignment problems. The model's performance is evaluated using waiting time and the number of people served. The model's performance is demonstrated numerically using the simulation-based case study. This study shows how to utilize a mathematical method to solve real-world problems with uncertainty and improve user welfare.


Agatz, N. A. H., Erera, A. L., Savelsbergh, M. W. P., & Wang, X. (2011). Dynamic ride-sharing: A simulation study in metro Atlanta. Transportation Research Part B: Methodological, 45(9), 1450–1464. https://doi.org/10.1016/j.trb.2011.05.017

Anindhita, W., Arisanty, M., & Rahmawati, D. (2016). Analisis Penerapan Teknologi Komunikasi Tepat Guna Pada Bisnis Transportasi Ojek Online. Prosiding Seminar Nasional Indocompac Universitas Bakrie, 2, 712–729.

Azizah, A., & Adawia, P. R. (2018). Analisis perkembangan industri transportasi online di era inovasi disruptif (Studi Kasus PT Gojek Indonesia). Cakrawala: Jurnal Humaniora Bina Sarana Informatika, 18(2), 149–156.

Chalermpong, S., Kato, H., Thaithatkul, P., Ratanawaraha, A., Fillone, A., Hoang-Tung, N., & Jittrapirom, P. (2023). Ride-hailing applications in Southeast Asia: A literature review. International Journal of Sustainable Transportation, 17(3), 298–318. https://doi.org/10.1080/15568318.2022.2032885

Chandler, C. (2019). Grab vs. Go-Jek: Inside Asia's battle of the 'superapps'. Fortune. In Retrieved Dec (Vol. 20, p. 2020).

Cramer, J., & Krueger, A. B. (2016). Disruptive Change in the Taxi Business: The Case of Uber. American Economic Review, 106(5), 177–182. https://doi.org/10.1257/aer.p20161002

Crittenden, A. B., Crittenden, V. L., & Crittenden, W. F. (2017). Industry Transformation via Channel Disruption. Journal of Marketing Channels, 24(1–2), 13–26. https://doi.org/10.1080/1046669X.2017.1346974

Do, M., Byun, W., Shin, D. K., & Jin, H. (2019). Factors Influencing Matching of Ride-Hailing Service Using Machine Learning Method. Sustainability, 11(20), 5–615. https://doi.org/10.3390/su11205615

Flores, O., & Rayle, L. (2017). How cities use regulation for innovation: The case of Uber, Lyft and Sidecar in San Francisco. Transportation Research Procedia, 25, 3756–3768.

Guo, X., Caros, N. S., & Zhao, J. (2021). Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand. Transportation Research Part B: Methodological, 150(1), 161–189. https://doi.org/10.1016/j.trb.2021.05.015

Huidobro, P., Alonso, P., Janiš, V., & Montes, S. (2022). Convexity and level sets for interval-valued fuzzy sets. Fuzzy Optimization and Decision Making, 21(4), 553–580. https://doi.org/10.1007/s10700-021-09376-7

Luo, Y., Jia, X., Fu, S., & Xu, M. (2019). pRide: Privacy-Preserving Ride Matching Over Road Networks for Online Ride-Hailing Service. IEEE Transactions on Information Forensics and Security, 14(7), 1791–1802. https://doi.org/10.1109/TIFS.2018.2885282

Lyu, G., Cheung, W. C., Teo, C.-P., & Wang, H. (2019). Multi-Objective Online Ride-Matching. SSRN Electronic Journal, 13(7), 2–47. https://doi.org/10.2139/ssrn.3356823

Megantara, T. R., Supian, S., & Chaerani, D. (2022). Strategies to Reduce Ride-Hailing Fuel Consumption Caused by Pick-Up Trips: A Mathematical Model under Uncertainty. Sustainability, 14(17), 10–648. https://doi.org/10.3390/su141710648

Nandi. (2019). The Influence of Online Transportation Application to the Mobility and Economic of the Society (Case Study on Using Grab and Go-Jek in Bandung, Indonesia). IOP Conference Series: Earth and Environmental Science, 286(1), 012034. https://doi.org/10.1088/1755-1315/286/1/012034

Qin, X., Yang, H., Wu, Y., & Zhu, H. (2021). Multi-party ride-matching problem in the ride-hailing market with bundled option services. Transportation Research Part C: Emerging Technologies, 131(1), 103–287. https://doi.org/10.1016/j.trc.2021.103287

Stiglic, M., Agatz, N., Savelsbergh, M., & Gradisar, M. (2015). The benefits of meeting points in ride-sharing systems. Transportation Research Part B: Methodological, 82(1), 36–53. https://doi.org/10.1016/j.trb.2015.07.025

Su, J.-S. (2007). Fuzzy programming based on interval-valued fuzzy numbers and ranking. International Journal Contemporary Mathematical Sciences, 2(8), 393–410.

Wibawa, B. M., Rahmawati, Y., & Rainaldo, M. (2018). Analisis Industri Bisnis Jasa Online Ride Sharing di Indonesia. Jurnal Bisnis Dan Manajemen, 8(1), 9–20.

Xu, Y., Wang, W., Xiong, G., Liu, X., Wu, W., & Liu, K. (2022). Network-Flow-Based Efficient Vehicle Dispatch for City-Scale Ride-Hailing Systems. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5526–5538. https://doi.org/10.1109/TITS.2021.3054893

Yan, C., Zhu, H., Korolko, N., & Woodard, D. (2020). Dynamic pricing and matching in ride‐hailing platforms. Naval Research Logistics (NRL), 67(8), 705–724. https://doi.org/10.1002/nav.21872

Yang, H., Qin, X., Ke, J., & Ye, J. (2020). Optimizing matching time interval and matching radius in on-demand ride-sourcing markets. Transportation Research Part B: Methodological, 131, 84–105. https://doi.org/10.1016/j.trb.2019.11.005

Young, M., & Farber, S. (2019). The who, why, and when of Uber and other ride-hailing trips: An examination of a large sample household travel survey. Transportation Research Part A: Policy and Practice, 119(1), 383–392. https://doi.org/10.1016/j.tra.2018.11.018

Yu, H., Jia, X., Zhang, H., & Shu, J. (2022). Efficient and Privacy-Preserving Ride Matching Using Exact Road Distance in Online Ride Hailing Services. IEEE Transactions on Services Computing, 15(4), 1841–1854. https://doi.org/10.1109/TSC.2020.3022875

Yu, H., Shu, J., Jia, X., Zhang, H., & Yu, X. (2019). lpRide: Lightweight and Privacy-Preserving Ride Matching Over Road Networks in Online Ride Hailing Systems. IEEE Transactions on Vehicular Technology, 68(11), 10418–10428. https://doi.org/10.1109/TVT.2019.2941761




How to Cite

Supian, S., Subiyanto, S., Megantara, T. R., & Bon, A. T. (2023). Ride-Hailing Assignment Problem under Waiting Time Uncertainty using Interval-Valued Fuzzy Quadratic. International Journal of Global Optimization and Its Application, 2(4), 209–220. https://doi.org/10.56225/ijgoia.v2i4.262
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