Dynamic Pricing for Carriers in Physical Internet with Peak Demand Forecasting

Bin Qiao (1) , Shenle Pan (1) , Eric Ballot (1)

1 CGS i3 - Centre de Gestion Scientifique i3

This paper investigates a dynamic pricing problem for less-than-truckload (LTL) carriers during several auction periods in Physical Internet (PI), in consideration of peak demand forecasting. PI can be considered as the interconnection of logistics networks via open PI-hubs, in which there are plenty of requests to be transported. These requests continuously arrive at different auction periods with various quantity. Carriers can bid for these requests through participating several rounds of auction. In a dynamic environment, a major problem for the carrier is how to decide the bidding price to maximize his profit. Besides, to make better decision, when determining the bidding price in current period, carrier should forecast the possible requests in next periods, especially facing with peak/off-peak time periods. This paper proposes a dynamic pricing model considering the forecasted quantity of requests in the next auction periods to optimize the bidding price and maximize the total profit. A numerical study is conducted to evaluate the model and study how the future requests influence the current pricing decision, including the influence to the bidding price and profit.

Mots clés: dynamic pricing; less-than-truckload; auction; demand forecasting; Physical Internet

Domaines: Recherche opérationnelle [math.OC]

Bin Qiao, Shenle Pan, Eric Ballot. Dynamic Pricing for Carriers in Physical Internet with Peak Demand Forecasting. 9th IFAC Conference MIM 2019, Aug 2019, Berlin, Germany. ⟨hal-02064744⟩