From imperfection to advantage: Quantifying the benefits of imperfect advance load information for multi-truck carriers

Abstract

Considering the dynamic and volatile conditions of spot markets, small trucking companies often struggle with load selection due to imperfect advance load information (iALI). This study develops a mathematical approach to better leverage iALI in the spot market. Using mathematical and statistical techniques, it examines two key aspects: (i) quantifying the benefit of iALI for multi-truck companies, and (ii) analyzing how market attributes affect its value. The proposed framework integrates iALI into truck activity planning via two decision-making policies: (i) Look-ahead (LOAH) and (ii) Value Function Approximation (VFA). LOAH assumes all loads materialize deterministically, while VFA uses a stochastic framework to dynamically incorporate imperfect information. To benchmark these policies, a Greedy policy is also considered as a baseline, where all advance load information is treated as completely unreliable, and decisions rely solely on currently available loads. To ensure practical relevance, the model includes real-world factors like domicile visits, truck coordination, and shipper classifications. Results show that VFA, by dynamically using iALI, improves profits by over 70% compared to LOAH, especially in classified markets, while also achieving faster solution times. A real-world case study confirms the model’s effectiveness for small trucking firms.