Service-oriented container slot allocation policy under stochastic demand - Liang, Jinpeng; Li, Liming; Zheng, Jianfeng; Tan, Zhijia (Transportation research. Part B: methodological, 2023)
The liner shipping industry plays a pivotal role in global cargo transportation, catering to both contract and spot shippers. Proper capacity allocation between these shippers is vital for maintaining service quality and improving revenue. This research investigates the service-oriented container slot allocation problem under stochastic demand, aiming to maximize total freight revenue while providing adequate service levels to contract shippers for sustaining their market loyalty. We use the fill rate (i.e., the proportion of satisfied demand) as a metric for service level and formulate the research problem as a stochastic linear programming model. To solve this model, we convert it into a multi-objective attainability problem by setting a target for total revenue, and apply Blackwell’s Approachability Theorem to theoretically determine the feasibility of any given revenue and service level requirements. Leveraging these insights, we devise near-optimal policies to guide the slot allocation decision under each demand scenario. Numerical experiments demonstrate that our approach outperforms the benchmark policies in the literature. Furthermore, it can also achieve near-optimal performance closely resembling the sampling average approximation (SAA) solution while significantly reducing the computational time. •The service-oriented slot allocation problem under stochastic demand is considered.•A stochastic linear programming model with service level constraints is proposed.•An efficient solution framework for the stochastic linear programming model is designed.•One of the near-optimal solutions with high computational efficiency is achieved.•A series of numerical experiments are carried out.