Bid price generation model for carriers in combinatorial transportation auction

Abstract

In combinatorial auction for truckload transportation service procurement, we introduce the bidding strategy for carrier facing the hard valuation problem to all possible routes. The regression model uses a bid-to-cost ratio of carriers surveyed in Thailand to represent the bidding behavior in combinatorial auction. This model facilitates carrier to value the bid price for interested packages that involve with pattern of transportation service under different competitive environment. The results of analysis with hypotheses by t-test reveal significantly that a pattern of transportation service, a number of competitors, and a pre-empty backhaul to new lane distance ratio with number of competitors do impact negatively on a bid-to-cost ratio of carrier, whereas a pre-empty backhaul to new lane distance ratio does impact positively on a bid-to-cost ratio of carrier in combinatorial auction. To find optimal bid price for interested packages in the incomplete information game, the research methodology in which captures monte-carlo simulation, regression model, winder determination problem and stochastic optimization model could provide the best solution to acquire the maximum expected profit. The results present that the benefit with optimal solution of bidder is more than the average market price in the competition considerably. While in turn the results also show that shipper could potentially reduce the cost of transport service procurement with this model.