A two-stage stochastic programming model for seaport berth and channel planning with uncertainties in ship arrival and handling times. (November 2022)

• Record Type: Journal Article
• Authors: Liu, Baoli; Li, Zhi-Chun; Wang, Yadong
• Is Part Of: Transportation research. Volume 167(2022)
• Publication Date: 2022-11
• Subjects: Port operations -- Berth allocation -- Ship scheduling -- Tugboat assignment -- Stochastic programming; Logistics -- Periodicals; Transportation -- Periodicals; 388.011
• DOI: 10.1016/j.tre.2022.102919
• Languages: English
• ISSNs: 1366-5545

Abstract

Highlights: Joint seaport berth and channel planning with uncertainties in ship arrival and handling times. Decomposition-based heuristic algorithm for two-stage programming model. The algorithm obtains optimality gap less than 3% for instances up to 500 scenarios and 190 ship movements. Abstract: The limited availability of berths and channels is generally the bottleneck restricting the capacity of a seaport and thus resulting in traffic congestion. Optimizing the operations of the berths and channels has been recognized as a more economic avenue for mitigating seaport traffic congestion compared with channel dredging and berth expanding that needs significant capital and time costs. This paper presents a two-stage stochastic mixed integer linear programming model for the seaport berth and channel planning, aiming to minimize the expected total weighted completion times of ships under uncertain ship arrival times and ship handling durations. The first stage decides the berth allocation of ships under uncertainty. In the second stage, the channel planning, including the selection of lanes, assignment of tugboats, and sequencing of ships, is determined after the uncertainty has been realized. To effectively solve the model, we propose two tailored decomposition methods, that is, the stage decomposition method and the decomposition-based heuristic algorithm (DHA). Then, a lower bound of the problem is derived to evaluate the quality of the solution. Numerical experiments on Tianjin Port of China show the satisfactory performance of these two proposed methods. Especially, the DHA is able to obtain near-optimal solutions with the average optimality gap less than 3% within four-hour computational time for the instances up to 500 scenarios and 190 ship movements. Some managerial insights are obtained to guide the operations of the port.