No Longer in the Dark: Utilizing Imperfect Advance Load Information for Single-Truck Operators

Mehdi Najafi () and Hossein Zolfagharinia ()
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Mehdi Najafi: Ted Rogers School of Management, Ryerson University, Toronto, Ontario M5G 2C3, Canada; Department of Industrial Engineering, Sharif University of Technology, Tehran 14588, Iran
Hossein Zolfagharinia: Ted Rogers School of Management, Ryerson University, Toronto, Ontario M5G 2C3, Canada

Transportation Science, 2022, vol. 56, issue 6, 1573-1597

Abstract: 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.

Keywords: long-haul transportation; imperfect information; advance load information; stochastic dynamic programming; single-truck operators (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:56:y:2022:i:6:p:1573-1597

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