摘要
堆场是自动化集装箱码头的重要组成部分,几乎所有的港口作业都与其密切关联,因此提高堆场作业效率是港口工作的重点。根据自动化集装箱码头堆场的分布特征以及制定堆存计划的特点,将自动化集装箱码头堆场箱区分为若干个子箱区,基于各子箱区容量的动态变化,提出自动化码头堆场空间动态分配模型,并使用遗传算法来求解。结果表明,通过该方法可以保证堆场内同一时间段进出口箱的均衡分配,实现了各子箱区之间以及箱区之间作业量的平衡。通过对该遗传算法和粒子群算法的求解结果进行比较分析,进一步验证了遗传算法的高效性和优越性。
The yard is an important part of the automated container terminal. Almost all of the port operations are closely related to it. Therefore, improving the operating efficiency of the yard is the focus of the port work. The automatic container terminal yard blocks were divided into several sub-blocks according to the distribution characteristics of the automated container terminal yard and the characteristics of the stacking plan. Based on the dynamic change of capacity of each sub-block, we proposed a dynamic space allocation model of the automated container yard, which was solved by genetic algorithm. The results show that this method can ensure the balanced distribution of the import and export containers in the storage yard at the same time, and realize the balance of the workload between sub-blocks and the blocks. The efficiency and superiority of the genetic algorithm are further verified by comparing and analyzing the t^sults of the genetic algorithm and particle swarm optimization.
作者
梁承姬
刘浩
张悦
Liang Chengji;Liu Hao;Zhang Yue(Logistics Research Center,Shanghai Maritime University,Shanghai 201306,China)
出处
《计算机应用与软件》
北大核心
2018年第10期1-8,共8页
Computer Applications and Software
基金
国家自然科学基金项目(71471110)
上海市科委创新项目(14170501500
16DZ1201402)
上海市重点学科项目(J50604)
陕西省社会科学基金项目(2015D060)
关键词
自动化码头
堆场
堆存策略
箱量平衡
遗传算法
Automated terminal
Yard Stacking strategies
Container workload balance GA
