Energy-Efficient model for integrated berth allocation and quay crane management

• Tackles integrated berth allocation and quay crane scheduling for optimal efficiency. • Includes vessel priority, crane maintenance, and energy consumption into scheduling. • Utilizes advanced multi-objective optimization algorithms, NSGA-II, MOGWO, and MOPSO. • Analyzes algorithm performance in minimizing service time and maximizing crane use. • Offers insights for sustainable, energy-efficient port operations and resource use. The challenge of allocating berths and assigning as well as scheduling quay cranes (QCs) is identified as one of the most important concerns of port operations, given that it involves many trade-offs for the improvement of efficiency. This research has explored this complex problem of QC scheduling with the incorporation of three operational conditions which have less attention in the literature: service priority of container vessels, QC preventive maintenance, and energy consumption. Such aspects become critical to ensuring that cranes are used effectively and in a safe and sustainable manner. The problem involves two main objectives: minimizing the handling and waiting costs of container vessels along with the energy costs of QC, and maximizing the utilization rate of QC powered by green or renewable energy. To address this multi-objective problem, three population-based multi-objective metaheuristics—NSGA-II, MOGWO, and MOPSO—are employed and comparatively analyzed in terms of their performance. Their performances are compared to various problem sizes with regard to their ability to produce a balance between service time minimization and maximum crane utilization objectives under prevailing conditions. Several numerical experiments in different dimensions are defined and implemented with both algorithms. The results analysis gives an overall perspective, while both algorithms show their value and strong points and weaknesses in meeting demanding requirements of modern container terminal logistics. The findings help in giving insights into developing more efficient, reliable, and sustainable strategies for optimizing berth resource management.