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

Sudradjat Supian; Subiyanto Subiyanto; Tubagus Robbi Megantara; Abdul Talib Bon

doi:10.56225/ijgoia.v2i4.262

Authors

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

DOI:

https://doi.org/10.56225/ijgoia.v2i4.262

Keywords:

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

Abstract

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.

References

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