A two-stage stochastic nonlinear integer-programming model for slot allocation of a liner container shipping service. (August 2021)

• Record Type: Journal Article
• Authors: Wang, Tingsong; Meng, Qiang; Wang, Shuaian; Qu, Xiaobo
• Is Part Of: Transportation research. Volume 150(2021)
• Page Start: 143
• Page End: 160
• Publication Date: 2021-08
• Subjects: Container slot allocation -- Two-stage stochastic mixed-integer nonlinear programming -- Sample average approximation -- Lagrangian relaxation and dual decomposition; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2021.04.016
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: A two-stage stochastic integer nonlinear programming model is proposed. A solution algorithm based on sample average approximation is used to solve the model. The proposed model can promote the shipping profit. The information of spot demand has significant impact on the earning of shipping profit. Abstract: In this study, we propose a container slot allocation problem for a liner shipping service. A liner containership provides a regular shipping service with a fixed itinerary and schedule. In practice, the liner containership may not be fully loaded, which results in a loss of revenue. We therefore segment shippers into two classes: contract shippers and spot shippers. A contract shipper has a contract with the shipping company and negotiates a fixed minimum quantity, so that the shipping company can secure a steady revenue. The remaining containership slots are open to spot shippers, allowing the shipping company to obtain ad hoc revenue. The container slot allocation problem is investigated in this study using a two-stage stochastic mixed-integer nonlinear programming model. We use the sample average approximation based on Lagrangian relaxation and dual decomposition techniques to effectively solve the model. Finally, we conduct a case study to evaluate the applicability and effectiveness of the proposed model and the solution algorithm.