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

Bin Qiao (), Shenle Pan () and Eric Ballot ()
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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

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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.

Keywords: Dynamic pricing; Less-than-truckload transport; Auction; Physical internet; Freight marketplace (search for similar items in EconPapers)
Date: 2019-10-01
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Citations: View citations in EconPapers (3)

Published in Journal of Intelligent Manufacturing, 2019, 30 (7), pp.2631-2643. ⟨10.1007/s10845-016-1289-8⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01414135

DOI: 10.1007/s10845-016-1289-8

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