Mathematical Programming-Driven Daily Berth Planning in Xiamen Port

Lu Zhen (), Haolin Li (), Liyang Xiao (), Dayu Lin () and Shuaian Wang ()
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Lu Zhen: School of Management, Shanghai University, Shanghai 200444, China
Haolin Li: School of Management, Shanghai University, Shanghai 200444, China
Liyang Xiao: School of Management, Shanghai University, Shanghai 200444, China
Dayu Lin: Xiamen Container Terminal Group, Xiamen, Fujian 361006, China
Shuaian Wang: Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China

Interfaces, 2024, vol. 54, issue 4, 329-356

Abstract: In this paper, we introduce the daily berth planning problem for Xiamen Hai-Tian Container Terminal (XHCT) at the Port of Xiamen, China, and propose the development and implementation of a berth planning system. The aim of the berth planning problem is to optimize daily berth plans by considering various decisions, including berth allocation, quay crane assignment, fairway traffic control, and berthing safety requirements. Among these decisions, the berthing safety requirement is a novel but practical problem in berth allocation that concerns the resource allocation related to berthing safety and interrelation with other decisions. A mathematical programming-driven methodological framework is designed with a 0-1 integer linear programming model for problem formulation and a highly efficient decomposition heuristic algorithm for solving the problem. This framework establishes the core for the berth planning system. The adoption of the berth planning system contributes to the increase of container throughput and berth capacity by transforming the planning process of XHCT. Moreover, the mathematical programming-driven daily berth planning informs further intelligent operations development in the Port of Xiamen and other container ports.

Keywords: daily berth planning; intelligent operations; mathematical programming; decomposition heuristic algorithm (search for similar items in EconPapers)
Date: 2024
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