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

truck on road under the cloudy sky

This research examined the effects of accessing advance information on a single-truck company's profits and other operational indicators, such as empty movements.

First, we developed a deterministic mathematical model and proposed a dynamic programming approach that takes into account imperfect advance load information (IALI). We then conducted experiments and used a dynamic mechanism to evaluate the potential benefits of using IALI.

The analysis showed that IALI could increase the single-truck company's profits by an average of 30%. Furthermore, other factors, such as the size of the operations, were found to impact the effectiveness of IALI. Additionally, when there were two classes of clients (with low and high cancelation risk), the potential benefits of IALI increased even more. Lastly, the developed method was applied to real-world data from a single-truck company operating in Ontario, Canada, to evaluate the benefits of IALI.