开放式车辆路径问题的改进Kruskal算法遗传搜索

J. Dutta, Partha Sarathi Barma, S. Kar, T. De
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引用次数: 14

摘要

本文提出了一种改进的Kruskal方法,以提高遗传算法确定从中心点出发的最小距离路径的效率,从而解决开放式车辆路径问题。在车辆路线问题中,车辆从一个中心点出发,几个客户被放置在不同的位置,以满足他们的需求,并返回中心点。在开放式车辆路径问题中,车辆在服务完客户后不返回中心点。面临的挑战是减少使用的车辆数量和同时行驶的距离。提出的方法采用遗传算法寻找特定车辆覆盖的客户集,并将提出的改进的Kruskal方法应用于局部路径优化。将新方法的结果与一些进化方法进行了比较分析。
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A Modified Kruskal's Algorithm to Improve Genetic Search for Open Vehicle Routing Problem
This article has proposed a modified Kruskal's method to increase the efficiency of a genetic algorithm to determine the path of least distance starting from a central point to solve the open vehicle routing problem. In a vehicle routing problem, vehicles start from a central point and several customers placed in different locations to serve their demands and return to the central point. In the case of the open vehicle routing problem, the vehicles do not go back to the central point after serving the customers. The challenge is to reduce the number of vehicles used and the distance travelled simultaneously. The proposed method applies genetic algorithms to find the set of customers those are covered by a particular vehicle and the authors have applied the proposed modified Kruskal's method for local routing optimization. The results of the new method are analyzed in comparison with some of the evolutionary methods.
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