Price determination model for full truckload operation under uncertainty

Abstract

Pricing is one of the fundamental management decisions required by a truckload carrier. Traditional pricing based on an average all relevant costs including fixed and variable costs is not capable of providing adequate margins that prevent losses during operation uncertainties inherent in truckload operation including demand variability and variation in service times. Hence, the objective of this study is to develop price determination model for full truckload operation under uncertainty. The research framework consists of two parts, simulation model and pricing model development. A simulation model is developed to capture the stochastic patterns inherent in the operation of full truckload network. This study utilizes Value at Risk (VaR) and Conditional Value at Risk (CVaR) as a measure of risk with significant advantage over Value at Risk (VaR), to full truckload pricing when conditions are unpredictable. The simulation and pricing model analyze results show that the company’s own trucks are given first priority for long-distance deliveries while outsourced trucks are reserved for short distance providing lower transportation cost and price. Pricing with 95% of VaR and 95% of CVaR is higher than traditional pricing. With different confident levels, pricing analysis results demonstrate that lower confidence levels conduct lower price in terms of price with VaR and CVaR. Moreover, increasing demand variation conducts higher full truckload pricing while reducing service time including waiting time, uploading and unloading time conducts lower price. The numerical analysis result from this study demonstrates a pricing method is suitable for transportation carriers who are risk averse. Transportation carriers in this group dislike risk and will stay away from high risk