针对集装箱码头闸口拥堵问题,建立集卡预约优化模型,目的是减少集卡排队等待时间和拥堵期间的碳排放。该模型在给定的集卡到达调整量水平的限制下,确定每个时段最优的预约份额,同时利用非平稳排队模型描述集卡到达随时间到达的特点。为求解模型,设计基于遗传算法与逐点固定流体近似算法(PSFFA)的求解方法,该算法利用遗传算法搜索最优解,基于PSFFA算法计算集卡排队时间。最后,利用算例对模型和算法的有效性进行了验证。结果表明,集卡预约优化模型可以有效地减少集卡排队时间,PSFFA方法可以较好地求解到达过程不平稳的排队问题。
Truck congestion of container terminals is an important issue which increases truck waiting time, decreases the operation efficiency of container terminal and increases the carbon emissions. Truck appointment is one of the effective methods to alleviate the gate congestion. In this paper, issues of truck appointment optimization are addressed. An optimization model for truck appointment quota is developed. In this model, the appointment quota for each time period is optimized, considering the constraints of adjustment quota. The non-stationary queuing model is used to describe the time-dependent characteristics of truck arrival. To solve the model, a method based genetic algorithm and Point wise Stationary Fluid Flow Approximation (PSFFA) is designed. Genetic algorithm is used to search the optimal solution and PSFFA is used to calculate the truck queuing time. In order to illustrate the validity of the proposed model and algorithms, numerical experiments with the data of one container terminal in Shenzhen from July 28 to August 2 in 2007. Results indicate that the proposed model can decrease the truck queuing time and PSFFA can solve the no-stationary queue problem efficiently. The optimization model developed in the paper can provide basis for decision-making of truck appointment, make a contribution to deepening the research on theory of truck appointment, and have a guiding significance in the practice of truck appointment.
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