考虑岸电分配的集装箱码头泊位调度优化研究

    Research on berth scheduling optimization of container terminal considering shore power distribution

    • 摘要: 为减少船舶在靠泊期间使用辅助发电机排放的污染气体,在岸电改造热潮下将岸电分配纳入集装箱码头泊位调度计划考虑。在传统泊位调度模型基础上,引入岸电分配相关约束和碳排放减少目标,建立包含船舶在港、岸电使用和碳排放在内的码头成本最小化为目标的数学模型。为实现问题的有效求解,提出带停滞变异策略的改进蝙蝠算法(IBA),并采用惯性权重方法更新个体寻优速度,防止算法陷入局部最优。算例研究表明:考虑岸电分配进一步提升泊位调度的难度,当船舶数量不超过25艘时,数学模型可实现精确求解且求解质量总是最优,当船舶数量增加到30艘时,数学模型则无法在限制时间内完成求解;与模型精确求解相比,IBA实现了所有算例的高效求解,其求解耗时明显少于模型,且求解结果与模型结果的最大偏差仅为2.37%;对比传统的遗传算法(GA)和基本蝙蝠算法(BA),IBA具有更好的求解性能,总能找到更高质量的解,其求解耗时优于GA,较基本蝙蝠算法平均仅约增加10 s。不同码头泊位及船舶岸电改造比例匹配分析发现,在固定的码头泊位岸电改造比例下,码头成本随着船舶岸电改造比例的提升呈下降趋势,但当两者比例达到均衡时,下降趋势平缓且基本保持不变。结果表明:只有当码头泊位与船舶岸电改造比例匹配时才能实现最优成本节约,保证供需平衡才能有效避免和减少岸电资源的浪费。

       

      Abstract: In order to reduce the emission of polluting gases from auxiliary generators during ship berthing, and in response to the growing adoption of shore power infrastructure, this paper incorporates the distribution of shore power into the berth scheduling plan of container terminals. Building upon the traditional berth scheduling model, relevant constraints for shore power allocation and carbon emission reduction targets are introduced, establishing a mathematical model that integrates ship-in-port activities, shore power usage, and carbon emissions. To solve the model effectively, an Improved Bat Algorithm(IBA) incorporating a stagnation mutation strategy is proposed. The inertia weight method is employed to update individual optimization speeds, preventing the algorithm from converging to local optima. Case studies show that considering shore power distribution increases the complexity of the berth scheduling problem. When the number of ships does not exceed 25, the mathematical model can be solved accurately with optimal solution quality; however, when the number increases to 30, the model cannot be solved within a reasonable time frame. In comparison, the IBA achieves efficient solutions for all test cases with significantly shorter computation times. The maximum deviation between IBA results and the exact model solutions is only 2. 37%. Furthermore, compared to traditional Genetic Algorithms and the basic Bat Algorithm, IBA demonstrates superior performance in terms of solution quality and computational efficiency, with an average increase in computation time of only about 10 seconds compared to the basic bat algorithm. A matching analysis between the shore power retrofit ratio of berths and ships revealed that under a fixed dock berth retrofit ratio, terminal costs decrease as the ship retrofit ratio increases. However, once the two ratios reach equilibrium, the rate of cost reduction levels off and remains largely stable. These results indicate that optimal cost savings are achieved when the shore power retrofit ratios of berths and ships are appropriately matched. Ensuring a balance between supply and demand can effectively prevent resource waste and enhance the efficiency of shore power utilization.

       

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