An improved model for precast component transportation scheduling under dynamic traffic and weather conditions

Transportation scheduling serves as a critical bridge between inventory management and the on-site assembly of precast components (PCs). However, dynamic traffic situations have been oversimplified in existing studies, posing challenges for accurate transportation scheduling. To promote just-in-time delivery of PCs, this study developed a modified dynamic scheduling model that accounts for varying traffic and weather conditions. Specifically, the model considers four distinct weather scenarios and urban road network dynamics to better align with real-world conditions. Case studies using the adaptive genetic algorithm were conducted to validate the model's performance across different weather conditions and delivery distances. Notable cost savings were achieved: 5.1%, 6.4%, 8.6%, and 9.7% for 0-50-km deliveries under sunny, foggy, rainy, and snowy conditions, with greater gains as conditions worsened, and 7.8%, 9.1%, 8.2%, and 6.3% for 50-100 km under the same weather conditions, where adverse weather reduced optimization gains due to longer travel times. The proposed model improves the accuracy of PC transportation travel time estimations and enhances scheduling efficiency, promoting just-in-time delivery and unlocking the potential benefits of precast construction.