IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i9p1355-d1385764.html
   My bibliography  Save this article

Ride-Hailing Matching with Uncertain Travel Time: A Novel Interval-Valued Fuzzy Multi-Objective Linear Programming Approach

Author

Listed:
  • Sudradjat Supian

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

  • Subiyanto

    (Department of Marine Science, Faculty of Fishery and Marine Science, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Tubagus Robbi Megantara

    (Doctoral Program in 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

This study introduces an innovative approach to tackle multi-objective linear programming (MOLP) problems amidst uncertainty, employing interval-valued fuzzy numbers. The method is tailored to resolve ride-hailing matching challenges encompassing uncertain travel times. Findings reveal that managing uncertainty parameters within interval-valued fuzzy MOLP is achieved through strategic reformulations, focusing on constraint coefficients, resulting in streamlined linear programming formulations conducive to solution simplicity. The efficacy of the proposed model in efficiently handling ride-hailing matching quandaries is demonstrated. Moreover, this study delves into the prospective applications of the developed method, including its potential for generalization to address non-linear programming (NLP) issues pertinent to the ride-hailing domain. This research advances decision-making processes under uncertainty and paves the way for broader applications beyond ride-hailing.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1355-:d:1385764
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/9/1355/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/9/1355/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    2. 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.
    3. Chiang, Jershan, 2001. "Fuzzy linear programming based on statistical confidence interval and interval-valued fuzzy set," European Journal of Operational Research, Elsevier, vol. 129(1), pages 65-86, February.
    4. Alemi, Farzad & Circella, Giovanni & Mokhtarian, Patricia & Handy, Susan, 2018. "Exploring the latent constructs behind the use of ridehailing in California," Journal of choice modelling, Elsevier, vol. 29(C), pages 47-62.
    5. Judd Cramer & Alan B. Krueger, 2016. "Disruptive Change in the Taxi Business: The Case of Uber," American Economic Review, American Economic Association, vol. 106(5), pages 177-182, May.
    6. 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.
    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. Gao, Chang & Lin, Xi & He, Fang & Tang, Xindi, 2024. "Online relocating and matching of ride-hailing services: A model-based modular approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).
    3. Zhang, Kenan & Nie, Yu (Marco), 2022. "Mitigating traffic congestion induced by transportation network companies: A policy analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 96-118.
    4. 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).
    5. Zhang, Kenan & Nie, Yu (Marco), 2021. "To pool or not to pool: Equilibrium, pricing and regulation," Transportation Research Part B: Methodological, Elsevier, vol. 151(C), pages 59-90.
    6. Nair, Gopindra S. & Bhat, Chandra R. & Batur, Irfan & Pendyala, Ram M. & Lam, William H.K., 2020. "A model of deadheading trips and pick-up locations for ride-hailing service vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 289-308.
    7. 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.
    8. Vuciterna, Rina & Thomsen, Michael & Popp, Jennie & Musliu, Arben, 2017. "Efficiency and Competitiveness of Kosovo Raspberry Producers," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252770, Southern Agricultural Economics Association.
    9. García-Herrera, Alisson & Basso, Leonardo J. & Tirachini, Alejandro, 2024. "Microeconomic analysis of ridesourcing market regulation policies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 186(C).
    10. Collan, Mikael, 2008. "New Method for Real Option Valuation Using Fuzzy Numbers," Working Papers 466, IAMSR, Åbo Akademi.
    11. Wenyao Niu & Yuan Rong & Liying Yu & Lu Huang, 2022. "A Novel Hybrid Group Decision Making Approach Based on EDAS and Regret Theory under a Fermatean Cubic Fuzzy Environment," Mathematics, MDPI, vol. 10(17), pages 1-30, August.
    12. de Andres-Sanchez, Jorge, 2007. "Claim reserving with fuzzy regression and Taylor's geometric separation method," Insurance: Mathematics and Economics, Elsevier, vol. 40(1), pages 145-163, January.
    13. Mikhailov, L., 2004. "A fuzzy approach to deriving priorities from interval pairwise comparison judgements," European Journal of Operational Research, Elsevier, vol. 159(3), pages 687-704, December.
    14. Vu, Khuong & Rohman, Ibrahim Kholilul & Bohlin, Erik, 2024. "Promoting the adoption of digital technology: Strategic policy insights from a network effects model," Telecommunications Policy, Elsevier, vol. 48(10).
    15. Hongyi Sun & Bingqian Zhang & Wenbin Ni, 2022. "A Hybrid Model Based on SEM and Fuzzy TOPSIS for Supplier Selection," Mathematics, MDPI, vol. 10(19), pages 1-19, September.
    16. 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).
    17. Berger, Thor & Chen, Chinchih & Frey, Carl Benedikt, 2018. "Drivers of disruption? Estimating the Uber effect," European Economic Review, Elsevier, vol. 110(C), pages 197-210.
    18. Liu, Yong-Jun & Zhang, Wei-Guo, 2015. "A multi-period fuzzy portfolio optimization model with minimum transaction lots," European Journal of Operational Research, Elsevier, vol. 242(3), pages 933-941.
    19. Sakawa, Masatoshi & Kato, Kosuke, 1998. "An interactive fuzzy satisficing method for structured multiobjective linear fractional programs with fuzzy numbers," European Journal of Operational Research, Elsevier, vol. 107(3), pages 575-589, June.
    20. Sajid Ali & Sang-Moon Lee & Choon-Man Jang, 2017. "Determination of the Most Optimal On-Shore Wind Farm Site Location Using a GIS-MCDM Methodology: Evaluating the Case of South Korea," Energies, MDPI, vol. 10(12), pages 1-22, December.

    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:9:p:1355-:d:1385764. 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.