IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v61y2024i3d10.1007_s12597-023-00729-x.html
   My bibliography  Save this article

Multi-objective and blockchain based optimization algorithm for fleet sharing management

Author

Listed:
  • Rashmi Bhardwaj

    (Guru Gobind Singh Indraprastha University (GGSIPU))

  • Shanky Garg

    (Guru Gobind Singh Indraprastha University (GGSIPU))

Abstract

With the rise in the demand for the fleet in today’s time even for covering a small distance, taxis play a crucial role not only in terms of ease in traveling but also in generating profit economically. Every advantage comes up with the side effects behind it such as the increase in profit along with the comfort of the customers have effects on the environment and also have issues related to the trust. Use of these services in excess sometimes poses a major threat to the environment in terms of doubling the traffic volume as the personal vehicles along with the hired vehicle volume are added together. It will also increase pollution which will affect our health. So, there is a need to devise a way that minimizes the negative effects of these services along with the increase in economic performance. This paper deals with the sharing ability of these platforms so that optimal allocation of passengers will be done to minimize the side effects and also to increase the overall usage of the fleet. However, the sharing ability of these services comes up with the disadvantage of having a trust issue among the users. So, to deal with this problem, we incorporate the blockchain concept here. This study includes the multi-objective functions such as the shortest route, maximal allocation of the passenger along with the minimization of cost which in turn reduces its effect on the environment, and minimization of trust-related problems with respect to the constraints such as demand, time, and supply. This study proposes two different optimization algorithms to solve this problem. The first one is based on considering one objective in each stage at a time where the objectives are dependent on each other whereas the second one is the formulation of a Multi-objective optimization algorithm by taking all the objectives from all the stages together with their respective weights by keeping in mind the problems related to these services. A case study is done based on this problem and solved using the computational approach with the first algorithm with the help of programming languages.

Suggested Citation

  • Rashmi Bhardwaj & Shanky Garg, 2024. "Multi-objective and blockchain based optimization algorithm for fleet sharing management," OPSEARCH, Springer;Operational Research Society of India, vol. 61(3), pages 1131-1153, September.
  • Handle: RePEc:spr:opsear:v:61:y:2024:i:3:d:10.1007_s12597-023-00729-x
    DOI: 10.1007/s12597-023-00729-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-023-00729-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12597-023-00729-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yi Cao & Shan Wang & Jinyang Li, 2021. "The Optimization Model of Ride-Sharing Route for Ride Hailing Considering Both System Optimization and User Fairness," Sustainability, MDPI, vol. 13(2), pages 1-17, January.
    2. 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.
    3. Agatz, Niels & Erera, Alan & Savelsbergh, Martin & Wang, Xing, 2012. "Optimization for dynamic ride-sharing: A review," European Journal of Operational Research, Elsevier, vol. 223(2), pages 295-303.
    4. Tatiana Babicheva & Wilco Burghout, 2019. "Empty vehicle redistribution in autonomous taxi services," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 745-767, December.
    5. Tamiz, Mehrdad & Jones, Dylan & Romero, Carlos, 1998. "Goal programming for decision making: An overview of the current state-of-the-art," European Journal of Operational Research, Elsevier, vol. 111(3), pages 569-581, December.
    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. Guo, Yuhan & Zhang, Yu & Boulaksil, Youssef & Qian, Yaguan & Allaoui, Hamid, 2023. "Modelling and analysis of online ride-sharing platforms – A sustainability perspective," European Journal of Operational Research, Elsevier, vol. 304(2), pages 577-595.
    2. Francisco Salas-Molina & Juan Antonio Rodr'iguez Aguilar & Filippo Bistaffa, 2020. "Shared value economics: an axiomatic approach," Papers 2006.00581, arXiv.org.
    3. Rong, Ke & Luo, Yining, 2023. "Toward born sharing: The sharing economy evolution enabled by the digital ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    4. Shibayama, Takeru & Emberger, Günter, 2020. "New mobility services: Taxonomy, innovation and the role of ICTs," Transport Policy, Elsevier, vol. 98(C), pages 79-90.
    5. Wang, Hai & Yang, Hai, 2019. "Ridesourcing systems: A framework and review," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 122-155.
    6. 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.
    7. Moon, Ilkyeong & Feng, Xuehao, 2017. "Supply chain coordination with a single supplier and multiple retailers considering customer arrival times and route selection," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 78-97.
    8. Scott Duke Kominers & Alexander Teytelboym & Vincent P Crawford, 2017. "An invitation to market design," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 33(4), pages 541-571.
    9. Wang, Wei & Miao, Wei & Liu, Yongdong & Deng, Yiting & Cao, Yunfei, 2022. "The impact of COVID-19 on the ride-sharing industry and its recovery: Causal evidence from China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 128-141.
    10. Tarduno, Matthew, 2021. "The congestion costs of Uber and Lyft," Journal of Urban Economics, Elsevier, vol. 122(C).
    11. Yu Wang & Shanyong Wang & Jing Wang & Jiuchang Wei & Chenglin Wang, 2020. "An empirical study of consumers’ intention to use ride-sharing services: using an extended technology acceptance model," Transportation, Springer, vol. 47(1), pages 397-415, February.
    12. Xu, Zhengtian & Yin, Yafeng & Zha, Liteng, 2017. "Optimal parking provision for ride-sourcing services," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 559-578.
    13. Jun Guan Neoh & Maxwell Chipulu & Alasdair Marshall, 2017. "What encourages people to carpool? An evaluation of factors with meta-analysis," Transportation, Springer, vol. 44(2), pages 423-447, March.
    14. Adam Millard-Ball & Liwei Liu & Whitney Hansen & Drew Cooper & Joe Castiglione, 2023. "Where ridehail drivers go between trips," Transportation, Springer, vol. 50(5), pages 1959-1981, October.
    15. 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.
    16. Carlin C. F. Chu & Simon S. W. Li, 2024. "A multiobjective optimization approach for threshold determination in extreme value analysis for financial time series," Computational Management Science, Springer, vol. 21(1), pages 1-14, June.
    17. Lei, Chao & Ouyang, Yanfeng, 2024. "Average minimum distance to visit a subset of random points in a compact region," Transportation Research Part B: Methodological, Elsevier, vol. 181(C).
    18. Wagner, Sebastian & Brandt, Tobias & Neumann, Dirk, 2016. "In free float: Developing Business Analytics support for carsharing providers," Omega, Elsevier, vol. 59(PA), pages 4-14.
    19. 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.
    20. Kräussl, Roman & Kräussl, Zsofia & Pollet, Joshua & Rinne, Kalle, 2024. "The performance of marketplace lenders," Journal of Banking & Finance, Elsevier, vol. 162(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:spr:opsear:v:61:y:2024:i:3:d:10.1007_s12597-023-00729-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.