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Self-organizing maps in population based metaheuristic to the dynamic vehicle routing problem

机译:基于人口的自组织映射对动态车辆路径问题的元启发式

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We consider the dynamic vehicle routing problem (dynamic VRP). In this problem, new customer demands are received along the day. Hence, they must be serviced at their locations by a set of vehicles in real time. The approach to address the problem is a hybrid method which manipulates the self-organizing map (SOM) neural network into a population based evolutionary algorithm. The method, called memetic SOM, illustrates how the concept of intermediate structure, also called elastic net or adaptive mesh concept, provided by the original SOM can naturally be applied into a dynamic setting. The experiments show that the heuristic outperforms the approaches that were applied to the Kilby et al. 22 problems with up to 385 customers. It performs better with respect to solution quality than the ant colony algorithm MACS-VRPTW, a genetic algorithm, and a multi-agent oriented approach, with a computation time used roughly 100 times lesser.
机译:我们考虑动态车辆路径问题(动态VRP)。在这个问题中,一天之内就收到了新的客户需求。因此,必须由一组车辆在其位置上对它们进行实时维修。解决该问题的方法是一种混合方法,它将自组织图(SOM)神经网络操纵为基于种群的进化算法。该方法称为Memetic SOM,它说明了如何将原始SOM提供的中间结构概念(也称为弹性网或自适应网格概念)自然地应用于动态设置。实验表明,启发式算法优于应用于Kilby等人的方法。多达385个客户的22个问题。与蚁群算法MACS-VRPTW,遗传算法和面向多主体的方法相比,它在解决方案质量方面的性能更好,而计算时间却减少了大约100倍。

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