A bidding advisory model for less-than-truckload combinatorial auctions with stochastic demand is proposed. The context of less-than-truckload combinatorial auctions is formulated and the first less-than-truckload bidding model is proposed; truckload models are improved by bidding with value-based pricing, volume segmentation, and uncertain demand and capacity. The less-than-truckload bidding model efficiently solves stochastic minimum-cost flow problems to construct profitable bids. Numerical experiments demonstrate its application and benefits.


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    Title :

    Pricing and Segmentation of Stochastic Demand in Less-Than-Truckload Combinatorial Bids


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2016-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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