基于遗传算子粒子群算法的拖轮动态调度

随着中国港口的发展,进出港口的船舶日益增多,使用拖轮的艘次逐渐增加.而当前极大部分港口所采用的基于人工经验的拖轮调度方案已难以保证船舶的顺利进出港口.如何根据复杂多变的进出港情况来制定合理的拖轮调度方案,已成为当前众多港口迫切需要解决的问题之一.通过分析港口拖轮作业过程与特点,建立了拖轮动态调度的数学模型,采用了基于动态遗传算子的改进粒子群优化算法对该模型进行求解.案例分析表明该拖轮动态调度模型是有效的.通过和传统粒子群算法对比分析,基于遗传算子的粒子群算法不仅在收敛速度上有明显的提高,而且求得的解更优.为港口拖轮动态调度的科学决策提供了依据.

The Dynamic Tugboat Schedule Based on Particle Swarm Algorithm Combined with Genetic Operators

With the port development of China and increasing numbers of vessels entering or leaving ports,there are more and more Tugboats been put into use.However,the tugboat dispatch schemes adopted by the majority of ports are based on experiences,which can’t assure vessels entering or leaving ports smoothly.How to formulate reasonable tugboat dispatch schemes in accordance with the complicated situation of entering or leaving ports has become one of the problems pressed for solution facing many ports.This Paper analyzes the process and characteristics of tugboat operation in ports,and sets up a mathematical model of dynamic tugboat dispatch.Then,the author adopts the Improved Particle Swarm Optimization combined with dynamic genetic operators to solve the model.The case analysis indicates that the model of dynamic tugboat’ dispatch is effective.According to the comparison with the original particle swarm optimization,the Particle Swarm Optimization combined with dynamic genetic operators not only has higher convergence rate,but also has more optimized solution.Therefore,this article provides a method for scientific decisions about dynamic tugboat dispatch in ports.