自适应遗传蜂群算法在集装箱码头集卡路径优化中的应用

发布时间：2018-04-05 01:11

本文选题：集装箱码头　切入点：人工蜂群算法　出处：《大连海事大学》2016年硕士论文

【摘要】：全球经济贸易的飞速发展,带来了物流业务量的急剧增长,据统计90%以上的国际贸易货物都要经过港口的中转运输,这之中的大部分都要通过集装箱运输来实现的。集装箱码头为了提高自身的经济效益,需要充分利用码头的各种资源设备,其中承担大部分码头水平运输任务的集装箱卡车的作业组织工作更是影响码头整体效率的关键技术之一。集卡路径优化问题是集卡作业组织中的一个重要组成部分。该问题是一个典型的复杂组合优化问题,对其进行优化求解,可有效提高集装箱码头集卡的作业效率,并降低集卡作业运营的成本,从而有利于码头整体经济效益的提升。本文针对集卡的路径优化问题,分析对比两种集卡作业模式,建立了面向"作业面"的基于成本的集卡路径优化模型。前人对集卡路径问题的求解多懫用数学规划等方法,存在着局部收敛、鲁棒性差等缺陷。本文采用较为新颖的人工蜂群算法(ABC)来对模型进行求解,期待获得较好的结果。人工蜂群算法具有结构简单,容易实现等优点,但作为群智能算法的一种,也存在易早熟、收敛速度慢等缺点,并且在以往的研究和应用中,蜂群算法多用于连续问题的求解。为了更好地解决上述属于离散规划的集卡路径优化问题,本文对基本蜂群算法进行了相关改进,提出一种自适应遗传蜂群算法(AGA-ABC)。其主要思想是,将遗传算法中的交叉和变异算子引入基本蜂群算法,对其加以改造,使得蜂群算法适用于求解离散优化问题。同时引入自适应因子,使算法在早期能有效避免早熟而后期又能加快算法向全局最优处收敛,从而提高算法的整体性能。为验证提出算法的性能,采用该算法对不同规模的经典TSP问题进行了测试与求解,所得结果验证了其可行性和优越性。进一步,将所提算法应用到建立的集卡路径优化模型中,分别在进口、出口双船舶到港和进出口单船舶到港,两种工况下进行了仿真测试,并对优化结果进行了相关分析。结果表明,提出的算法能在满足各种性能约束的前提下,有效的对集卡行驶路径进行优化,得到上述问题的令人满意的工程优化结果。工作表明,提出的算法对集卡路径优化问题是有效的,可以得到较优越的优化结果,以作为实际集卡作业组织的参考。本文的研究具有一定的理论意义和应用价值。
[Abstract]:The rapid development of the global economy and trade, bring the rapid growth of the logistics business volume, according to statistics, more than 90% of international trade in goods must pass through the port of transshipment, the majority must realize through the container transport container terminal. In order to improve their economic benefits, to make full use of various resources equipment terminal. The container truck transport task level wharf to bear most of the operation organization work is one of the key technologies affecting the efficiency of the wharf. The whole truck path optimization problem is an important part of the truck operation organization. This problem is a typical combinatorial optimization problem, for solving it, can effectively improve the container terminal the optimal operation efficiency, and reduce the truck operation cost of operation, which is conducive to the overall economic efficiency improved. Aiming at the truck dock The path optimization problem, comparison and analysis of two kinds of truck operation mode, for the establishment of "operation" truck path optimization model based on cost. To solve the problem of the previous truck path multi Zhi by mathematical programming method, the existence of local convergence, defects and poor robustness. This paper uses artificial bee colony algorithm is novel (ABC) to solve the model, to obtain better results. The artificial bee colony algorithm has the advantages of simple structure, easy to implement, but as a kind of swarm intelligence algorithm, is also easy to premature and slow convergence speed and other shortcomings, and in the previous research and applications, bee colony algorithm usually used to solve continuous problems in order to better solve the above belongs to the discrete planning path optimization of container trucks, some improvements on the basic bee colony algorithm, proposed an adaptive genetic ant colony algorithm (AGA-ABC). The main idea is to, The genetic algorithm crossover and mutation operator into the basic ABC algorithm to transform it, makes the colony algorithm suitable for solving discrete optimization problems. At the same time, the introduction of adaptive factor, so that the algorithm can effectively avoid the premature convergence and the later stage can accelerate the convergence to the global optimum in the early stage, so as to improve the overall performance of the algorithm. The proposed algorithm in order to verify the performance of the classical TSP problem, the different scale of the algorithm is tested and solved, and the results verify the feasibility and superiority. Further, the proposed algorithm is applied to establish the truck path optimization model, respectively in the import, export and import and export to Hong Kong ship double single ship to Hong Kong. Two conditions to carry out the simulation test, and the optimization results are analyzed. The results show that the proposed algorithm can satisfy the performance constraints in the premise, effective for truck driving Path optimization, the problem of satisfactory optimization results. Engineering work shows that the proposed algorithm is effective for path optimization of container trucks, can get better optimization results, as the actual truck operation organization. This study has a certain theoretical significance and application value.

【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U691.3;TP18

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