基于改进蚁群算法的集装箱装卸顺序优化研究

Research on improved Ant Colony Algorithm based optimization of container handling sequence problem

为缩短船舶在港时间,提高码头的作业效率,应用改进蚁群算法对集装箱装卸顺序的组合优化问题进行求解。首先结合柔性作业车间调度理论与集装箱装卸过程中船舶和岸桥的实际情形,建立集装箱装卸顺序调度模型;然后针对基本蚁群算法易出现早熟现象和收敛速度慢等问题,通过动态的改变信息素的挥发度与信息素强度,同时按照改进的信息素更新策略更新各路径的信息素,从而跳出局部最优;最后运用C#.NET语言对基于改进蚁群算法的集装箱装卸顺序问题进行仿真与步骤分析,验证了改进蚁群算法的有效性。实践证明,改进后的蚁群算法基本上克服了传统算法自身的不足,能够对集装箱装卸顺序优化,缩短作业时间。

We apply improved Ant Colony Algorithm to combinatorial optimization problem in order to shorten port waiting time and improve port efficiency. We initially constructed a mathematical model based on flexible job shop scheduling theory and the real situation of the ship and container crane. We then updated the pheromone of every-path based on new updating rule to jump out of its local optimism through dynamically improving pheromone volatility and pheromone intensity. We eventually performed simulation and steps analysis with C#. NET to verify its effectiveness. Experimental results show that it can optimize the sequence of container loading and unloading and shorten operation time