Pricing and Segmentation of Stochastic Demand in Truckload Combinatorial Bids

A bidding advisory model for truckload combinatorial auctions with stochastic demand (BMoTS) is proposed hereby. The contributions of this work are: (1) using value-based pricing for bidding rules, (2) segmenting the maximum volume of demand that a carrier is willing to serve, and (3) incorporating demand uncertainty. The algorithm efficiently solves stochastic minimum-cost flow problems and constructs bids that combine synergetic lanes in profitable tours. A numerical experiment illustrates the application of BMoTS.