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Dynamic pricing model for less-than-truckload carriers in the Physical Internet

Author

Listed:
  • Bin Qiao

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Shenle Pan

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Eric Ballot

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper investigates a less-than-truckload dynamic pricing decision-making problem in the context of the Physical Internet (PI). The PI can be seen as the interconnection of logistics networks via open PI-hubs. In terms of transport, PI-hubs can be considered as spot freight markets where LTL requests with different volumes/destinations continuously arrive over time and only remain for short periods. Carriers can bid for these requests using short-term contracts. In a dynamic, stochastic environment like this, a major concern for carriers is how to propose prices for requests to maximise their revenue. The latter is determined by the proposed price and the probability of winning the request at that price. This paper proposes a dynamic pricing model based on an auction mechanism to optimise the carrier's bid price. An experimental study is conducted in which two pricing strategies are proposed and assessed: a unique bidding price (one unique price for all requests at an auction), and a variable bidding price (price for each request at an auction). Three influencing factors are also investigated: quantity of requests, carrier capacity, and cost. The experimental results provide insightful conclusions and useful guidelines for carriers regarding pricing decisions in PI-hubs.

Suggested Citation

  • Bin Qiao & Shenle Pan & Eric Ballot, 2019. "Dynamic pricing model for less-than-truckload carriers in the Physical Internet," Post-Print hal-01414135, HAL.
    • Handle: RePEc:hal:journl:hal-01414135
    DOI: 10.1007/s10845-016-1289-8
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    Citations

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    Cited by:

    1. Guo, Chaojie & Thompson, Russell G. & Foliente, Greg & Kong, Xiang T.R., 2021. "An auction-enabled collaborative routing mechanism for omnichannel on-demand logistics through transshipment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).
    2. Lafkihi, Mariam & Pan, Shenle & Ballot, Eric, 2019. "Freight transportation service procurement: A literature review and future research opportunities in omnichannel E-commerce," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 348-365.
    3. Feyza Gürbüz & İkbal Eski & Berrin Denizhan & Cihan Dağlı, 2019. "Prediction of damage parameters of a 3PL company via data mining and neural networks," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1437-1449, March.

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