| 基于遗传算法的自动化集装箱码头多载AGV调度 |
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| 引用本文： | 霍凯歌,胡志华.基于遗传算法的自动化集装箱码头多载AGV调度[J].上海海事大学学报,2016,37(3):7-12. |
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| 作者姓名： | 霍凯歌 胡志华 |
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| 作者单位： | 上海海事大学 物流科学与工程研究院,上海海事大学 物流科学与工程研究院 |
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| 基金项目： | 国家自然科学基金（71101088，71471109）；交通运输部应用基础研究项目（2015329810260）；上海市曙光计划（13SG48） |
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| 摘 要： | 为提升自动化集装箱码头的作业效率，减轻码头吞吐量增大带来的交通问题，降低自动化导引小车（Automated Guided Vehicle, AGV)的空载率，在自动化集装箱码头应用可以同时搬运不止一个集装箱的多载AGV，建立多载AGV调度问题的混合整数线性规划（Mixed Integer Linear Programming, MILP)模型，应用遗传算法进行求解.借助算例，对比遗传算法与MILP算法的求解效果，分析交叉概率和变异概率对遗传算法的影响，比较多载AGV与单载AGV的作业时间，验证遗传算法的可靠性.该方法表明，遗传算法不仅求解效率高，而且对MILP算法不适用的大、中型多载AGV调度问题，也能给出值得信赖的近似最优解.
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| 关 键 词： | 自动化集装箱码头 多载自动化引导小车(AGV) 混合整数线性规划（MILP） 遗传算法 |
| 收稿时间： | 2015/10/14 0:00:00 |
| 修稿时间： | 2015/12/27 0:00:00 |
Multi-load AGV scheduling in automated container terminals |
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| Huo Kaige and Hu Zhihua.Multi-load AGV scheduling in automated container terminals[J].Journal of Shanghai Maritime University,2016,37(3):7-12. |
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| Authors: | Huo Kaige and Hu Zhihua |
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| Institution: | Shanghai Maritime University Logistics Research Center and Shanghai Maritime University Logistics Research Center |
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| Abstract: | In order to improve work efficiency of automatic container terminals, relieve traffic problem caused by increased handling capacity, and lower empty load ratio of Automated Guided Vehicles (AGVs), the multi load AGVs that can move more than one container at the same time are used in automated container terminals, a Mixed Integer Linear Programming (MILP) model is established for the multi load AGV scheduling issue, and the genetic algorithm is applied to solve the model. Through examples, the solving effects of the genetic algorithm and the MILP algorithm are compared, the impacts of crossover probability and mutation probability on the genetic algorithm are analyzed, the working times between multi load AGVs and single load AGVs are compared, and the reliability of the genetic algorithm is verified. The method indicates that the genetic algorithm is not only effective in solving the issues but also capable of giving the reliable and approximate optimal solutions to large and medium scaled AGV scheduling issues to which the MILP algorithm is inappropriate. |
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| Keywords: | automated container terminal multi load Automated Guided Vehicle (AGV) Mixed Integer Linear Programming (MILP) genetic algorithm |
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