IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v48y2012i1p190-201.html
   My bibliography  Save this article

Scheduling commercial vehicle queues at a Canada–US border crossing

Author

Listed:
  • Haughton, Michael
  • Sapna Isotupa, K.P.

Abstract

Using the context of queue operations at a major Canada–US commercial border crossing for truck-borne trade flows, we report on a computer simulation study to predict the likely impacts of smoothing those flows. We quantify the operational and resource efficiencies of smoothing for trans-border trucking companies and their trans-border supply chain partners as well as for government authorities with regulatory jurisdiction at border crossings. Our study’s major conclusion is that smoothing can achieve queue performance levels that, in the absence of smoothing, would require significant investment in truck processing capacity at border crossings.

Suggested Citation

  • Haughton, Michael & Sapna Isotupa, K.P., 2012. "Scheduling commercial vehicle queues at a Canada–US border crossing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 190-201.
  • Handle: RePEc:eee:transe:v:48:y:2012:i:1:p:190-201
    DOI: 10.1016/j.tre.2011.07.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554511001037
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tre.2011.07.008?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. Miao, Zhaowei & Lim, Andrew & Ma, Hong, 2009. "Truck dock assignment problem with operational time constraint within crossdocks," European Journal of Operational Research, Elsevier, vol. 192(1), pages 105-115, January.
    2. Martin A. Lariviere & Jan A. Van Mieghem, 2004. "Strategically Seeking Service: How Competition Can Generate Poisson Arrivals," Manufacturing & Service Operations Management, INFORMS, vol. 6(1), pages 23-40, January.
    3. Federico Sabria & Carlos F. Daganzo, 1989. "Approximate Expressions for Queueing Systems with Scheduled Arrivals and Established Service Order," Transportation Science, INFORMS, vol. 23(3), pages 159-165, August.
    4. Joanne C. Bennett & D. J. Worthington, 1998. "An Example of a Good but Partially Successful OR Engagement: Improving Outpatient Clinic Operations," Interfaces, INFORMS, vol. 28(5), pages 56-69, October.
    5. Yigal Gerchak & Diwakar Gupta & Mordechai Henig, 1996. "Reservation Planning for Elective Surgery Under Uncertain Demand for Emergency Surgery," Management Science, INFORMS, vol. 42(3), pages 321-334, March.
    6. Linda V. Green & Sergei Savin, 2008. "Reducing Delays for Medical Appointments: A Queueing Approach," Operations Research, INFORMS, vol. 56(6), pages 1526-1538, December.
    7. S. Creemers & M. Lambrecht, 2009. "An advanced queueing model to analyze appointment-driven service systems," Post-Print hal-00800200, HAL.
    8. Babad, Yair M. & Dada, Maqbool & Saharia, Aditya N., 1996. "An appointment-based service center with guaranteed service," European Journal of Operational Research, Elsevier, vol. 89(2), pages 246-258, March.
    9. Zohar Feldman & Avishai Mandelbaum & William A. Massey & Ward Whitt, 2008. "Staffing of Time-Varying Queues to Achieve Time-Stable Performance," Management Science, INFORMS, vol. 54(2), pages 324-338, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. A. Azadeh & M. S. Naghavi lhoseiny & V. Salehi, 2018. "Optimum alternatives of tandem G/G/K queues with disaster customers and retrial phenomenon: interactive voice response systems," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 68(3), pages 535-562, July.
    2. Ilker Topcu, Y. & Ulengin, Fusun & Kabak, Özgür & Ekici, Sule Onsel & Unver, Berna, 2020. "A decision support methodology for increasing the efficiency of the largest border crossing between Europe and Turkey," Research in Transportation Economics, Elsevier, vol. 80(C).

    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. Navid Izady, 2015. "Appointment Capacity Planning in Specialty Clinics: A Queueing Approach," Operations Research, INFORMS, vol. 63(4), pages 916-930, August.
    2. Jacob Feldman & Nan Liu & Huseyin Topaloglu & Serhan Ziya, 2014. "Appointment Scheduling Under Patient Preference and No-Show Behavior," Operations Research, INFORMS, vol. 62(4), pages 794-811, August.
    3. Van-Anh Truong, 2015. "Optimal Advance Scheduling," Management Science, INFORMS, vol. 61(7), pages 1584-1597, July.
    4. Jingui Xie & Weifen Zhuang & Marcus Ang & Mabel C. Chou & Li Luo & David D. Yao, 2021. "Analytics for Hospital Resource Planning—Two Case Studies," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1863-1885, June.
    5. Creemers, Stefan & Lambrecht, Marc R. & Beliën, Jeroen & Van den Broeke, Maud, 2021. "Evaluation of appointment scheduling rules: A multi-performance measurement approach," Omega, Elsevier, vol. 100(C).
    6. Nguyen, Thu Ba T. & Sivakumar, Appa Iyer & Graves, Stephen C., 2018. "Capacity planning with demand uncertainty for outpatient clinics," European Journal of Operational Research, Elsevier, vol. 267(1), pages 338-348.
    7. Linda V. Green, 2012. "OM Forum--The Vital Role of Operations Analysis in Improving Healthcare Delivery," Manufacturing & Service Operations Management, INFORMS, vol. 14(4), pages 488-494, October.
    8. De Vuyst, Stijn & Bruneel, Herwig & Fiems, Dieter, 2014. "Computationally efficient evaluation of appointment schedules in health care," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1142-1154.
    9. Philipp Afèche & Opher Baron & Joseph Milner & Ricky Roet-Green, 2019. "Pricing and Prioritizing Time-Sensitive Customers with Heterogeneous Demand Rates," Operations Research, INFORMS, vol. 67(4), pages 1184-1208, July.
    10. Rouba Ibrahim & Ward Whitt, 2011. "Wait-Time Predictors for Customer Service Systems with Time-Varying Demand and Capacity," Operations Research, INFORMS, vol. 59(5), pages 1106-1118, October.
    11. Niyirora, Jerome & Zhuang, Jun, 2017. "Fluid approximations and control of queues in emergency departments," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1110-1124.
    12. H. Khorshidian & M. Akbarpour Shirazi & S. M. T. Fatemi Ghomi, 2019. "An intelligent truck scheduling and transportation planning optimization model for product portfolio in a cross-dock," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 163-184, January.
    13. Xi Chen & Dave Worthington, 2017. "Staffing of time-varying queues using a geometric discrete time modelling approach," Annals of Operations Research, Springer, vol. 252(1), pages 63-84, May.
    14. Andrew Lim & Brian Rodrigues & Fei Xiao & Yi Zhu, 2004. "Crane scheduling with spatial constraints," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(3), pages 386-406, April.
    15. Achal Bassamboo & Assaf Zeevi, 2009. "On a Data-Driven Method for Staffing Large Call Centers," Operations Research, INFORMS, vol. 57(3), pages 714-726, June.
    16. Dietz, Dennis C., 2011. "Practical scheduling for call center operations," Omega, Elsevier, vol. 39(5), pages 550-557, October.
    17. Yunan Liu & Ward Whitt, 2012. "Stabilizing Customer Abandonment in Many-Server Queues with Time-Varying Arrivals," Operations Research, INFORMS, vol. 60(6), pages 1551-1564, December.
    18. Shenghai Zhou & Yichuan Ding & Woonghee Tim Huh & Guohua Wan, 2021. "Constant Job‐Allowance Policies for Appointment Scheduling: Performance Bounds and Numerical Analysis," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2211-2231, July.
    19. Sauré, Antoine & Patrick, Jonathan & Tyldesley, Scott & Puterman, Martin L., 2012. "Dynamic multi-appointment patient scheduling for radiation therapy," European Journal of Operational Research, Elsevier, vol. 223(2), pages 573-584.
    20. Merve Bodur & James R. Luedtke, 2017. "Mixed-Integer Rounding Enhanced Benders Decomposition for Multiclass Service-System Staffing and Scheduling with Arrival Rate Uncertainty," Management Science, INFORMS, vol. 63(7), pages 2073-2091, July.

    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:eee:transe:v:48:y:2012:i:1:p:190-201. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.