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Tri-Vizor Uses an Efficient Algorithm to Identify Collaborative Shipping Opportunities

Author

Listed:
  • Stefan Creemers

    (IÉSEG School of Management, 59000 Lille, France; and Research Center for Operations Management, KU Leuven, 3000 Leuven, Belgium)

  • Gert Woumans

    (IÉSEG School of Management, 59000 Lille, France; and Research Center for Operations Management, KU Leuven, 3000 Leuven, Belgium)

  • Robert Boute

    (Technology and Operations Management Area, Vlerick Business School, 9000 Ghent, Belgium; and Research Center for Operations Management, KU Leuven, 3000 Leuven, Belgium)

  • Jeroen Beliën

    (Research Center for Information Management, Modelling and Simulation, KU Leuven, 1000 Brussels, Belgium)

Abstract

Collaborative shipping programs, whereby companies bundle their transport loads, are a growing trend in logistics. By bundling shipments with other partners, available space in truck hauls for one company can be used to transport shipments for other companies. The benefits are reduced logistics costs and a lower carbon footprint. Although the advantages of collaborative shipping are clear, finding suitable collaboration partners is a major impediment. In this article we present a tool that enables the quick identification of potential partners based on their geographical compatibility, even when the database of shipment lanes is very large. The tool allows the detection of bundling, back-hauling, and round-trip opportunities, as well as “collect-and-or-drop” opportunities in which shipments are collected and (or) dropped off en route. Tri-Vizor, a facilitator and orchestrator of horizontal logistics partnerships, is currently using this tool. Any company that is looking for collaborative shipping partners would also find it valuable. For Tri-Vizor, whose database has grown to over 130,000 shipment lanes, this tool has become an indispensable asset in detecting collaborative shipping opportunities.

Suggested Citation

  • Stefan Creemers & Gert Woumans & Robert Boute & Jeroen Beliën, 2017. "Tri-Vizor Uses an Efficient Algorithm to Identify Collaborative Shipping Opportunities," Interfaces, INFORMS, vol. 47(3), pages 244-259, June.
    • Handle: RePEc:inm:orinte:v:47:y:2017:i:3:p:244-259
    DOI: 10.1287/inte.2016.0878
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    References listed on IDEAS

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    1. Vanovermeire, Christine & Sörensen, Kenneth, 2014. "Measuring and rewarding flexibility in collaborative distribution, including two-partner coalitions," European Journal of Operational Research, Elsevier, vol. 239(1), pages 157-165.
    2. Shenle Pan & Eric Ballot & Frédéric Fontane, 2013. "The reduction of greenhouse gas emissions from freight transport by pooling supply chains," Post-Print hal-00733678, HAL.
    3. M. W. P. Savelsbergh & M. Sol, 1995. "The General Pickup and Delivery Problem," Transportation Science, INFORMS, vol. 29(1), pages 17-29, February.
    4. Pan, Shenle & Ballot, Eric & Fontane, Frédéric, 2013. "The reduction of greenhouse gas emissions from freight transport by pooling supply chains," International Journal of Production Economics, Elsevier, vol. 143(1), pages 86-94.
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    1. Kant, Goos & van Beest, Ilja & Brekelmans, Ruud, 2021. "A Holistic Approach to Identify Collaborative Shipping Opportunities," Other publications TiSEM 4b65a129-4093-40b3-82b7-2, Tilburg University, School of Economics and Management.
    2. Ben Jouida, Sihem & Guajardo, Mario & Klibi, Walid & Krichen, Saoussen, 2021. "Profit maximizing coalitions with shared capacities in distribution networks," European Journal of Operational Research, Elsevier, vol. 288(2), pages 480-495.
    3. Hacardiaux, Thomas & Defryn, Christof & Tancrez, Jean-Sébastien & Verdonck, Lotte, 2020. "Balancing partner preferences for logistics costs and carbon footprint in a horizontal cooperation," Research Memorandum 002, Maastricht University, Graduate School of Business and Economics (GSBE).
    4. De Moor, Bram J. & Creemers, Stefan & Boute, Robert N., 2023. "Breaking truck dominance in supply chains: Proactive freight consolidation and modal split transport," International Journal of Production Economics, Elsevier, vol. 257(C).
    5. Öner, Nihat & Kuyzu, Gültekin, 2021. "Core stable coalition selection in collaborative truckload transportation procurement," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    6. Shenle Pan & Damien Trentesaux & Eric Ballot & George Q. Huang, 2019. "Horizontal collaborative transport: survey of solutions and practical implementation issues," Post-Print hal-02008934, HAL.
    7. Thomas Hacardiaux & Christof Defryn & Jean-Sébastien Tancrez & Lotte Verdonck, 2022. "Balancing partner preferences for logistics costs and carbon footprint in a horizontal cooperation," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 121-153, March.
    8. Creemers, Stefan & Boute, Robert, 2022. "The joint replenishment problem: Optimal policy and exact evaluation method," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1175-1188.
    9. Thomas Hacardiaux & Jean-Sébastien Tancrez, 2020. "Assessing the environmental benefits of horizontal cooperation using a location-inventory model," 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. 28(4), pages 1363-1387, December.
    10. Boshuai Zhao & Roel Leus, 2022. "An improved decomposition-based heuristic for truck platooning," Papers 2210.05562, arXiv.org, revised Feb 2023.

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