A Hybridized Thompson Sampling Hyperheuristic for the Truck Scheduling Problem at a Cross-Docking Facility

Cross-docking has become a widely adopted logistics strategy for improving distribution efficiency by reducing inventory requirements and accelerating freight transshipment across supply chains. However, efficient terminal operations require reliable coordination of inbound and outbound truck activities under tightly synchronized and time-sensitive operating conditions, making the development of efficient service schedules highly challenging in practice. This study investigates the truck scheduling problem at a mixed-mode cross-docking facility. A mixed-integer linear programming model aimed at reducing total operational costs is adopted. To effectively solve this computationally complex problem, a Hybridized Thompson Sampling Hyperheuristic (HTSH) algorithm is developed, which integrates a population-based evolutionary framework with a Thompson Sampling-based adaptive control mechanism that dynamically learns and selects search operators according to observed search performance. A mixed repair-penalization infeasibility handling strategy is incorporated to efficiently manage inbound-outbound coordination constraints. Moreover, a hybrid fitness evaluation mechanism combining fast approximate assessment with exact optimization evaluation is applied for efficient exploration together with accurate solution refinement. A set of extensive computational experiments demonstrates that HTSH consistently produces high-quality scheduling solutions comparable with exact optimization on small instances and substantially outperforms a set of classical and more recent metaheuristics across a wide range of problem instances. The HTSH algorithm is able to achieve improvement margins of up to approximately 24% while maintaining an average runtime of 186.71 s. The experimental results demonstrate that the proposed HTSH framework provides an effective and scalable solution approach for cross-docking truck scheduling, contributing to improved operational efficiency and service reliability in distribution terminal operations.