IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v47y2017i5p372-395.html
   My bibliography  Save this article

Calibrated Route Finder: Improving the Safety, Environmental Consciousness, and Cost Effectiveness of Truck Routing in Sweden

Author

Listed:
  • Mikael Rönnqvist

    (Forestry Research Institute of Sweden, SE-75183 Uppsala, Sweden; and Université Laval, Québec City, Québec G1V 0A6, Canada)

  • Gunnar Svenson

    (Forestry Research Institute of Sweden, SE-75183 Uppsala, Sweden)

  • Patrik Flisberg

    (Forestry Research Institute of Sweden, SE-75183 Uppsala, Sweden)

  • Lars-Erik Jönsson

    (SDC, SE-85183 Sundsvall, Sweden)

Abstract

Calibrated Route Finder (CRF), an online route generation system, successfully finds the best route when many conflicting objectives are involved by using analytics in a collaborative environment. CRF, which has been in use since 2009, uses many diverse big data sources, which must be revised continuously. One of its key features is its use of an innovative inverse optimization process that establishes more than 100 weights to balance distance, speed, social values, environmental impacts, traffic safety, driver stress, fuel consumption, CO 2 emissions, and costs. The system enables the measurement of hilliness and curvature and incorporates rules that consider legal and practical issues related to routing in and around cities, turning in intersections, time delays, fuel consumption, and CO 2 emissions that result from waiting, acceleration, and braking. The system is used by all major forest companies in Sweden and in 60 percent of the two million annual transports in this sector. It has resulted in a paradigm shift from manual, imprecise, and unilaterally determined routes to automatically determined routes, which the stakeholders determine jointly. It has also enabled standardization, promoted collaboration, and reduced costs, thus strengthening the competitiveness of the Swedish forest industry in the international market.

Suggested Citation

  • Mikael Rönnqvist & Gunnar Svenson & Patrik Flisberg & Lars-Erik Jönsson, 2017. "Calibrated Route Finder: Improving the Safety, Environmental Consciousness, and Cost Effectiveness of Truck Routing in Sweden," Interfaces, INFORMS, vol. 47(5), pages 372-395, October.
    • Handle: RePEc:inm:orinte:v:47:y:2017:i:5:p:372-395
    DOI: 10.1287/inte.2017.0906
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/inte.2017.0906
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2017.0906?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. Ravindra K. Ahuja & James B. Orlin, 2001. "Inverse Optimization," Operations Research, INFORMS, vol. 49(5), pages 771-783, October.
    2. Frisk, M. & Göthe-Lundgren, M. & Jörnsten, K. & Rönnqvist, M., 2010. "Cost allocation in collaborative forest transportation," European Journal of Operational Research, Elsevier, vol. 205(2), pages 448-458, September.
    3. Clemens Heuberger, 2004. "Inverse Combinatorial Optimization: A Survey on Problems, Methods, and Results," Journal of Combinatorial Optimization, Springer, vol. 8(3), pages 329-361, September.
    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. Bhaskar, Umang & Ligett, Katrina & Schulman, Leonard J. & Swamy, Chaitanya, 2019. "Achieving target equilibria in network routing games without knowing the latency functions," Games and Economic Behavior, Elsevier, vol. 118(C), pages 533-569.
    2. Bennet Gebken & Sebastian Peitz, 2021. "Inverse multiobjective optimization: Inferring decision criteria from data," Journal of Global Optimization, Springer, vol. 80(1), pages 3-29, May.
    3. Abumoslem Mohammadi & Javad Tayyebi, 2019. "Maximum Capacity Path Interdiction Problem with Fixed Costs," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(04), pages 1-21, August.
    4. Jonathan Yu-Meng Li, 2021. "Inverse Optimization of Convex Risk Functions," Management Science, INFORMS, vol. 67(11), pages 7113-7141, November.
    5. Timothy C. Y. Chan & Tim Craig & Taewoo Lee & Michael B. Sharpe, 2014. "Generalized Inverse Multiobjective Optimization with Application to Cancer Therapy," Operations Research, INFORMS, vol. 62(3), pages 680-695, June.
    6. Kien Trung Nguyen & Nguyen Thanh Hung, 2020. "The inverse connected p-median problem on block graphs under various cost functions," Annals of Operations Research, Springer, vol. 292(1), pages 97-112, September.
    7. Nguyen, Kien Trung & Hung, Nguyen Thanh, 2021. "The minmax regret inverse maximum weight problem," Applied Mathematics and Computation, Elsevier, vol. 407(C).
    8. Zeynep Erkin & Matthew D. Bailey & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2010. "Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach," Decision Analysis, INFORMS, vol. 7(4), pages 358-365, December.
    9. Chassein, André & Goerigk, Marc, 2018. "Variable-sized uncertainty and inverse problems in robust optimization," European Journal of Operational Research, Elsevier, vol. 264(1), pages 17-28.
    10. Vincent Mousseau & Özgür Özpeynirci & Selin Özpeynirci, 2018. "Inverse multiple criteria sorting problem," Annals of Operations Research, Springer, vol. 267(1), pages 379-412, August.
    11. Aswani, Anil & Kaminsky, Philip & Mintz, Yonatan & Flowers, Elena & Fukuoka, Yoshimi, 2019. "Behavioral modeling in weight loss interventions," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1058-1072.
    12. Merve Bodur & Timothy C. Y. Chan & Ian Yihang Zhu, 2022. "Inverse Mixed Integer Optimization: Polyhedral Insights and Trust Region Methods," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1471-1488, May.
    13. T. R. Wang & N. Pedroni & E. Zio & V. Mousseau, 2020. "Identification of Protective Actions to Reduce the Vulnerability of Safety‐Critical Systems to Malevolent Intentional Acts: An Optimization‐Based Decision‐Making Approach," Risk Analysis, John Wiley & Sons, vol. 40(3), pages 565-587, March.
    14. Susan Jia Xu & Mehdi Nourinejad & Xuebo Lai & Joseph Y. J. Chow, 2018. "Network Learning via Multiagent Inverse Transportation Problems," Service Science, INFORMS, vol. 52(6), pages 1347-1364, December.
    15. Timothy C. Y. Chan & Katharina Forster & Steven Habbous & Claire Holloway & Luciano Ieraci & Yusuf Shalaby & Nasrin Yousefi, 2022. "Inverse optimization on hierarchical networks: an application to breast cancer clinical pathways," Health Care Management Science, Springer, vol. 25(4), pages 590-622, December.
    16. Mintz, Yonatan & Aswani, Anil & Kaminsky, Philip & Flowers, Elena & Fukuoka, Yoshimi, 2023. "Behavioral analytics for myopic agents," European Journal of Operational Research, Elsevier, vol. 310(2), pages 793-811.
    17. Javad Tayyebi & Ali Reza Sepasian, 2020. "Partial inverse min–max spanning tree problem," Journal of Combinatorial Optimization, Springer, vol. 40(4), pages 1075-1091, November.
    18. Rico Walter & Martin Wirth & Alexander Lawrinenko, 2017. "Improved approaches to the exact solution of the machine covering problem," Journal of Scheduling, Springer, vol. 20(2), pages 147-164, April.
    19. András Kovács, 2021. "Inverse optimization approach to the identification of electricity consumer models," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(2), pages 521-537, June.
    20. Rishabh Gupta & Qi Zhang, 2022. "Decomposition and Adaptive Sampling for Data-Driven Inverse Linear Optimization," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2720-2735, September.

    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:47:y:2017:i:5:p:372-395. 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.