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This research aims to optimize berth allocation and quay crane assignment in container terminals to enhance operational efficiency.

July 7, 2026Scientific Reports0 citationsOpen Access

Integrated continuous berth allocation with time-invariant specific quay crane assignment using a mixed-integer model and greedy genetic algorithm

YZYan ZhouGuangdong Polytechnic of Science and Technology XCXu ChengShenyang Agricultural University YCYu CaoShandong University

Key Points

This research aims to optimize berth allocation and quay crane assignment in container terminals to enhance operational efficiency.
Developed a mixed-integer programming model to minimize vessel port time.
Created a greedy genetic algorithm for efficient scheduling with operational constraints.
Conducted computational experiments using real and synthetic operational data.
The method produced high-quality feasible schedules within practical computation times.
Greedy initialization and repair mechanisms significantly improved solution quality and stability.
Experiments indicated the approach is effective for integrated seaside scheduling problems.

Abstract

Efficient coordination of berth and quay crane resources is essential for improving the operational performance of container terminals under increasing vessel traffic and limited shoreline capacity. This paper studies an integrated optimization problem that combines continuous berth allocation with time-invariant specific quay crane assignment. Unlike studies that determine only the number of quay cranes assigned to each vessel, this work explicitly determines the identities of assigned quay cranes while considering practical operational constraints, including continuous berth positions, vessel-specific crane quantity limits, contiguous crane assignment, vessel non-overlap, crane capacity, and crane non-crossing requirements. A mixed-integer programming model is formulated to minimize the total time that vessels spend in port. To solve medium- and large-scale instances efficiently, a greedy genetic algorithm is developed by combining greedy initialization, evolutionary search, decoding-based feasibility checking, and repair operations for infeasible offspring. The algorithm is designed to preserve useful inherited information while restoring feasibility with respect to berth-space and crane-resource constraints. Computational experiments are conducted using real operational data from a container port in Liaoning, China, together with synthetic instances of different scales. The results show that the proposed method can obtain high-quality feasible schedules within practical computation times. Additional ablation experiments demonstrate that both greedy initialization and repair contribute to performance improvement, with the repair mechanism providing the most evident gain in average solution quality and stability. These findings indicate that the proposed approach is suitable for integrated seaside scheduling problems requiring explicit crane identity decisions and fast feasible-schedule generation.

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