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考虑实际生活中带多种扩展特征(如多车场、多车型、客户服务优先级、时间窗等)的车辆路径问题应用广泛,建立带软时间窗多车场多车型车辆路径问题的数学模型,并提出一种改进的蚁群优化算法(IACO)求解该模型.首先,根据就近原则将客户分组,并通过扫描算法构造初始路径;其次,通过引入遗传算子并自适应地调整交叉概率和变异概率来提高算法的全局收敛能力,且采用平滑机制来提高蚁群优化算法的性能;最后,采用3-opt策略来提高算法的局部搜索能力.将提出的算法应用在3个随机产生的实例中,仿真表明提出的IACO在收敛速度和解质量两方面都优于现有的3种算法,证明提出的算法是有效可行的,且提出的模型具有一定的实际意义.
Considering the extensive application of vehicle routing problems with many extended features (such as multi-car parks, multi-models, customer service priorities, time windows, etc.) in real life, a mathematical model of multi-vehicle vehicle routing problem with multi-car windows with soft time windows An improved ant colony optimization algorithm (IACO) is proposed to solve this model. Firstly, customers are grouped according to the nearest principle, and the initial path is constructed by scanning algorithm. Secondly, by introducing genetic operators and adaptively adjusting crossover probability and mutation probability To improve the global convergence ability of the algorithm and use the smoothing mechanism to improve the performance of the ant colony optimization algorithm.Finally, the 3-opt strategy is used to improve the local search ability of the algorithm.The proposed algorithm is applied to three randomly generated instances, Simulation results show that the proposed IACO outperforms the existing three algorithms in both the convergence rate and the solution quality, and proves that the proposed algorithm is effective and feasible. The proposed model has some practical significance.