No Longer in the Dark: Utilizing Imperfect Advance Load Information for Single-Truck Operators
研究单卡车公司如何利用不完善的提前货物信息提高利润、减少空驶，通过数学模型和动态规划方法验证了平均利润可提升近30%，并分析了网络规模和托运人风险类别的影响。
This study investigates how imperfect advance load information (IALI) can improve profits and other operational indicators, such as empty movements, for a single-truck company. To analyze the value of IALI, we first develop a deterministic mathematical model. Then, we propose a stochastic dynamic programming approach that can utilize IALI. After designing a comprehensive set of experiments, we employ both models using a dynamic implementation mechanism to assess the benefits of using IALI. Our statistical analysis reveals that (1) utilizing IALI can significantly improve a single-truck company’s profits, by as much as almost 30% on average, and (2) the impact of using IALI can be affected by other factors (e.g., network size). In another set of experiments, we examine the benefits of IALI in a new environment where there are two classes of shippers, high risk and low risk. The results suggest that the potential benefits can be even larger with two classes of shippers. Last, we collect data over two three-week periods for a single-truck company that operates in Ontario, Canada, and we apply our methods for evaluating the benefits of IALI.