Container slot allocation and dynamic pricing of time-sensitive cargoes considering port congestion and uncertain demand

•Container slot allocation and pricing problem with uncertain shipping demand.•Two chance-constrained programming models under two slot allocation strategies.•A tailored SAA-RLT solution algorithm to solve the models. This paper studies the container slot allocation problem for time-sensitive cargoe...

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Veröffentlicht in:Transportation research. Part E, Logistics and transportation review Jg. 144; S. 102149
Hauptverfasser: Wang, Tingsong, Tian, Xuecheng, Wang, Yadong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.12.2020
Schlagworte:
Container slot allocation
Dynamic pricing
Port congestion
Time-sensitive cargoes
Uncertain demand
Dynamic pricing
Port congestion
Uncertain demand
Container slot allocation
Time-sensitive cargoes
ISSN:1366-5545, 1878-5794
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Zusammenfassung:•Container slot allocation and pricing problem with uncertain shipping demand.•Two chance-constrained programming models under two slot allocation strategies.•A tailored SAA-RLT solution algorithm to solve the models. This paper studies the container slot allocation problem for time-sensitive cargoes with consideration of dynamic pricing and port congestion under uncertain demand. Time-sensitive cargoes call for express delivery. However, port congestion is a non-negligible factor to affect the delivery time. Hence, a new freight rate function with respect to the delivery time considering port congestion is proposed for time-sensitive cargoes, and two different slot allocation strategies are proposed: Loyal Strategy and Expansive Strategy. Accordingly, we formulate the problem as a one-stage container slot allocation model and a two-stage container slot allocation model, respectively. Both of the two models are stochastic mixed-integer quadratic programming models with chance constraints due to the involvement of dynamic pricing, port congestion, and uncertain demand. To solve the proposed models, a tailored algorithm that combines the Sample Average Approximation approach and the Reformulation Linearization Technique (SAA-RLT) is developed in this paper. Finally, numerical experiments are carried out to verify the applicability and effectiveness of the proposed models and the solution algorithm.
ISSN:1366-5545
1878-5794
DOI:10.1016/j.tre.2020.102149