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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