This study explores optimizing high-speed railway (HSR) meal services, a unique logistical challenge requiring precise alignment with train departure times. Unlike standard delivery systems, HSR services demand strict on-time delivery, balancing the conflicting costs of earliness and tardiness while accounting for the stochastic nature of preparation and delivery processes.
A stochastic single-machine scheduling model is developed to minimize the expected costs of earliness and tardiness in HSR meal delivery. The problem is formulated as a two-stage stochastic mixed-binary program, incorporating uncertainties and intermodal coordination. A surrogate algorithm is proposed to enhance computational efficiency, particularly for large problem sizes. Extensive numerical experiments based on real-world scenarios are conducted to validate the model and algorithm.
The surrogate algorithm significantly improves computational efficiency while maintaining high solution accuracy. It outperforms commercial solvers for large sample sizes and highlights the importance of incorporating uncertainties. Particularly, as the sample size increases, this algorithm can even match the optimal solution (i.e. 0% of the performance gap) with a 63.594% reduction in computation time.
This study bridges the gap in integrating synchromodal logistics principles into HSR meal services. It provides innovative methodologies for synchronizing operations across transport modes, addressing both conflicting cost objectives and system uncertainties. The findings offer actionable insights for optimizing time-sensitive, intermodal logistics in the HSR industry and beyond.
