What question did this study set out to answer?

This study aims to develop a sustainable scheduling framework for optimizing vessel operations in port terminals.

June 12, 2026

Multi-objective sustainable berth and quay crane scheduling optimization considering berth task attributes in marine logistics support terminals

Key Points

This study aims to develop a sustainable scheduling framework for optimizing vessel operations in port terminals.
A mixed-integer programming model is constructed to balance vessel delays, employee overtime, and resource utilization.
An improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) is implemented to find high-quality schedules.
Computational experiments utilize real-world data from a maritime logistics support terminal to validate the model.
The NSGA-II optimization reduces total vessel delay time by 87.95%, employee overtime by 82.25%, and improves resource utilization by 17.02%.
The proposed method consistently outperforms traditional scheduling algorithms across different dataset scales.
Analysis indicates that FCFS decreases overtime while EDES mitigates vessel delay risks.

Abstract

Efficient and sustainable scheduling of port terminals is critical for enhancing the environmental and operational performance of maritime logistics systems. This study aims to establish a multi-objective sustainable scheduling framework that simultaneously addresses vessel delays, employee overtime, and resource utilisation balance within the context of marine petroleum logistics support supply chains (MPLSSC), incorporating berth task attributes (BTA) that reflect real operational characteristics. A mixed-integer programming model is developed to optimise these three objectives, explicitly considering BTA such as task priority, mutual exclusion, and berth shifting relationships among vessels. To tackle the computational complexity, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to obtain high-quality Pareto-optimal solutions. Computational experiments based on real-world data from a logistics support terminal demonstrate the efficacy of the proposed approach. Compared to historical manual schedules, the enhanced NSGA-II achieves substantial improvements, optimising total vessel delay time, employee overtime, and resource utilisation balance by an average of 87.95%, 82.25%, and 17.02%, respectively. Moreover, it consistently outperforms traditional NSGA-II and the MOSA algorithm across various dataset scales. Furthermore, strategy analysis reveals that the first-come-first-served (FCFS) rule effectively mitigates overtime, while the earlier-departure-earlier-service (EDES) strategy reduces vessel delay risks. Uncertainties in task preparation time, berth shifting time, vessel arrival delays, and task execution speeds influence the sustainability of integrated berth and quay crane scheduling, with each parameter affecting the objective in distinct ways. This research enriches the literature on berth-quay crane scheduling optimisation (BQSO) by integrating sustainability and task-attribute considerations, offering practical managerial insights and decision-support tools for sustainable operations management in logistics support terminals.

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