IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v55y2025i1p66-82.html

Optimizing Mobility for Elderly and Disabled Dutch Citizens Using Taxis

Author

Listed:
  • Frans J. C. T. de Ruiter

    (Consultants in Quantitative Methods (CQM), 5616 RM Eindhoven, Netherlands; and Department of Operations Research and Logistics, Wageningen University, 6706 KN Wageningen, Netherlands)

  • Johan M. M. van Rooij

    (Department of Information and Computing Sciences, Utrecht University, 3584 CC Utrecht, Netherlands)

  • Peter Hulsen

    (Consultants in Quantitative Methods (CQM), 5616 RM Eindhoven, Netherlands)

  • Bart Post

    (Consultants in Quantitative Methods (CQM), 5616 RM Eindhoven, Netherlands)

  • Jeroen Goes

    (Consultants in Quantitative Methods (CQM), 5616 RM Eindhoven, Netherlands)

  • Geert Teeuwen

    (Consultants in Quantitative Methods (CQM), 5616 RM Eindhoven, Netherlands)

  • Matthijs Tijink

    (Consultants in Quantitative Methods (CQM), 5616 RM Eindhoven, Netherlands)

  • Bart Verberne

    (Consultants in Quantitative Methods (CQM), 5616 RM Eindhoven, Netherlands)

  • Niels Bourgonjen

    (Geodan (part of Sogelink), 1079 MB Amsterdam, Netherlands)

  • Roelf Nienhuis

    (Transvision B.V., 2909 LC Capelle aan den IJssel, Netherlands)

  • Tjeerd van der Poel

    (Transvision B.V., 2909 LC Capelle aan den IJssel, Netherlands)

  • Laurens van Remortele

    (Transvision B.V., 2909 LC Capelle aan den IJssel, Netherlands)

Abstract

In the Netherlands, 200,000 elderly and disabled citizens annually use subsidized taxi rides executed by Transvision. The day-to-day planning of up to 15,000 long-distance rides was previously a complex and daunting task split over dozens of subcontractors. Transvision, CQM, and Geodan developed an optimization solution that combines the rides into efficient taxi routes. Starting in January 2020, this solution significantly improved the mobility challenge for elderly and disabled citizens, including (1) increased punctuality and a 50% improvement in passenger satisfaction, (2) savings of 15 million driving kilometers per year, and (3) combined financial savings for all stakeholders of 60 million euros over the years 2019 to 2023 and another total of 30 million euros projected for 2024 and 2025, according to conservative estimates. Daily planning in a single batch can range from 1,000 to 15,000 rides. To construct high-quality ride plans in reasonable time for this massive-scale operations research problem, we applied classical operations research techniques viewed through a modern lens. In this paper, we explain how practical large-scale dial-a-ride problems can be solved using high-quality heuristics that exploit the power of parallel processing. Furthermore, we present new and efficient techniques to perform the required millions to billions of calculations to determine distances and driving times on the Dutch road network. We overcome several practical challenges such as (1) aligning the interests of a vulnerable passenger group and over 60 different taxi operators, (2) aligning the software that interfaces with the various companies, and (3) adapting to changing regulations and ad hoc COVID-19 measures.

Suggested Citation

  • Frans J. C. T. de Ruiter & Johan M. M. van Rooij & Peter Hulsen & Bart Post & Jeroen Goes & Geert Teeuwen & Matthijs Tijink & Bart Verberne & Niels Bourgonjen & Roelf Nienhuis & Tjeerd van der Poel & , 2025. "Optimizing Mobility for Elderly and Disabled Dutch Citizens Using Taxis," Interfaces, INFORMS, vol. 55(1), pages 66-82, January.
  • Handle: RePEc:inm:orinte:v:55:y:2025:i:1:p:66-82
    DOI: 10.1287/inte.2024.0180
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.2024.0180
    Download Restriction: no

    File URL: https://libkey.io/10.1287/inte.2024.0180?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
    ---><---

    References listed on IDEAS

    as
    1. Gerhard Reinelt, 1991. "TSPLIB—A Traveling Salesman Problem Library," INFORMS Journal on Computing, INFORMS, vol. 3(4), pages 376-384, November.
    2. Jean-François Cordeau, 2006. "A Branch-and-Cut Algorithm for the Dial-a-Ride Problem," Operations Research, INFORMS, vol. 54(3), pages 573-586, June.
    3. Schryen, Guido, 2020. "Parallel computational optimization in operations research: A new integrative framework, literature review and research directions," European Journal of Operational Research, Elsevier, vol. 287(1), pages 1-18.
    4. Jean-François Cordeau & Gilbert Laporte, 2007. "The dial-a-ride problem: models and algorithms," Annals of Operations Research, Springer, vol. 153(1), pages 29-46, September.
    5. Baldacci, Roberto & Mingozzi, Aristide & Roberti, Roberto, 2012. "Recent exact algorithms for solving the vehicle routing problem under capacity and time window constraints," European Journal of Operational Research, Elsevier, vol. 218(1), pages 1-6.
    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. Malaguti, Enrico & Martello, Silvano & Santini, Alberto, 2018. "The traveling salesman problem with pickups, deliveries, and draft limits," Omega, Elsevier, vol. 74(C), pages 50-58.
    2. Luciano Costa & Claudio Contardo & Guy Desaulniers, 2019. "Exact Branch-Price-and-Cut Algorithms for Vehicle Routing," Transportation Science, INFORMS, vol. 53(4), pages 946-985, July.
    3. Roberto Baldacci & Andrew Lim & Emiliano Traversi & Roberto Wolfler Calvo, 2020. "Optimal Solution of Vehicle Routing Problems with Fractional Objective Function," Transportation Science, INFORMS, vol. 54(2), pages 434-452, March.
    4. Detti, Paolo & Papalini, Francesco & Lara, Garazi Zabalo Manrique de, 2017. "A multi-depot dial-a-ride problem with heterogeneous vehicles and compatibility constraints in healthcare," Omega, Elsevier, vol. 70(C), pages 1-14.
    5. S Salhi & A Al-Khedhairi, 2010. "Integrating heuristic information into exact methods: The case of the vertex p-centre problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(11), pages 1619-1631, November.
    6. Shangyao Yan & Chun-Ying Chen & Chuan-Che Wu, 2012. "Solution methods for the taxi pooling problem," Transportation, Springer, vol. 39(3), pages 723-748, May.
    7. Shi, Zhiyuan & Hong, Shaozhi & Wang, Zeling & Li, Ang, 2026. "Exact solution approaches for the traveling salesman problem with a drone station," European Journal of Operational Research, Elsevier, vol. 328(3), pages 845-861.
    8. Rafael Blanquero & Emilio Carrizosa & Amaya Nogales-Gómez & Frank Plastria, 2014. "Single-facility huff location problems on networks," Annals of Operations Research, Springer, vol. 222(1), pages 175-195, November.
    9. Timo Gschwind & Stefan Irnich, 2012. "Effective Handling of Dynamic Time Windows and Synchronization with Precedences for Exact Vehicle Routing," Working Papers 1211, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    10. Martins, Francisco Leonardo Bezerra & do Nascimento, José Cláudio, 2022. "Power law dynamics in genealogical graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    11. Marjan Marzban & Qian-Ping Gu & Xiaohua Jia, 2016. "New analysis and computational study for the planar connected dominating set problem," Journal of Combinatorial Optimization, Springer, vol. 32(1), pages 198-225, July.
    12. Su, Yue & Dupin, Nicolas & Parragh, Sophie N. & Puchinger, Jakob, 2024. "A Branch-and-Price algorithm for the electric autonomous Dial-A-Ride Problem," Transportation Research Part B: Methodological, Elsevier, vol. 186(C).
    13. Ferrer, José M. & Martín-Campo, F. Javier & Ortuño, M. Teresa & Pedraza-Martínez, Alfonso J. & Tirado, Gregorio & Vitoriano, Begoña, 2018. "Multi-criteria optimization for last mile distribution of disaster relief aid: Test cases and applications," European Journal of Operational Research, Elsevier, vol. 269(2), pages 501-515.
    14. R. Baldacci & E. Hadjiconstantinou & A. Mingozzi, 2004. "An Exact Algorithm for the Capacitated Vehicle Routing Problem Based on a Two-Commodity Network Flow Formulation," Operations Research, INFORMS, vol. 52(5), pages 723-738, October.
    15. Roberto Tadei & Guido Perboli & Francesca Perfetti, 2017. "The multi-path Traveling Salesman Problem with stochastic travel costs," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(1), pages 3-23, March.
    16. Timo Gschwind & Michael Drexl, 2016. "Adaptive Large Neighborhood Search with a Constant-Time Feasibility Test for the Dial-a-Ride Problem," Working Papers 1624, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    17. Lancia, Giuseppe & Vidoni, Paolo, 2020. "Finding the largest triangle in a graph in expected quadratic time," European Journal of Operational Research, Elsevier, vol. 286(2), pages 458-467.
    18. Oya Ekin Karaşan & A. Ridha Mahjoub & Onur Özkök & Hande Yaman, 2014. "Survivability in Hierarchical Telecommunications Networks Under Dual Homing," INFORMS Journal on Computing, INFORMS, vol. 26(1), pages 1-15, February.
    19. Paredes-Belmar, Germán & Montero, Elizabeth & Lüer-Villagra, Armin & Marianov, Vladimir & Araya-Sassi, Claudio, 2022. "Vehicle routing for milk collection with gradual blending: A case arising in Chile," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1403-1416.
    20. Thanh Tan Doan & Nathalie Bostel & Minh Hoàng Hà & Vu Hoang Vuong Nguyen, 2023. "New mixed integer linear programming models and an iterated local search for the clustered traveling salesman problem with relaxed priority rule," Journal of Combinatorial Optimization, Springer, vol. 46(1), pages 1-27, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:inm:orinte:v:55:y:2025:i:1:p:66-82. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.