Implementation of Genetic Algorithm in Completion Traveling Salesman Problem Study Case of Garuda Express Delivery (GED)

Syariful Alim, M. M. Putra, Bagus Damas Ardilestian, A. Bintoro
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Abstract

Indaily distribution activities, GED is a shipping service company. Always associated with couriers as inter mediaries. Where each courier will send a packagetoseveraldifferent places.Surely they(couriers)want to immediately complete their tasks,by findingor determining the path that is traversed in order to shorten the work and return to the office and then make a report. This case is commonly called the Traveling Salesman Problem,which can be solved by several methods. One of them is by optimization of Genetic Algorithms. Genetic Algorithm methods can provide solutions to these problems by providing input (input) fromseveral addresses that they will (courier) distribute. Then the input will be processed with several stages starting from initialization, selection, crossover, mutation and regeneration. The results are then displayed in graphical form which links the shipping addresses. The results of the study, obtained the fastest route with a maximumof 10 points or shipping address,which can be used by the courier in its distribution. In this study, the objective valueisthe value of the length of the road section taken from the Google Map. With termination rules or conditions that state that the smallest and largest fitness values must be the same or 60% of the fitness of the genetic algorithm population shows the greatest fitness. This rule will give the same and accurate results even though the number of generations produced is different.
遗传算法在完成旅行商问题中的实现——以鹰航快递(GED)为例
在日常配送活动中,GED是一家航运服务公司。总是与快递员作为中介联系在一起。每个快递员将把一个包裹送到几个不同的地方。当然,他们(快递员)想要立即完成他们的任务,通过找到或确定所穿越的路径,以缩短工作时间,然后返回办公室,然后做报告。这种情况通常被称为旅行推销员问题,它可以通过几种方法来解决。其中之一是通过遗传算法的优化。遗传算法方法可以通过提供来自几个地址的输入(输入)来解决这些问题,这些地址将被(信使)分配。然后,输入将经过初始化、选择、交叉、突变和再生等几个阶段进行处理。然后以图形形式显示结果,其中链接送货地址。研究的结果,获得了最快的路线,最多10点或送货地址,这可以被快递公司在其配送中使用。在本研究中,目标值是取自谷歌地图的路段长度值。终止规则或条件规定最小和最大适应度值必须相同,或者遗传算法种群适应度的60%显示最大适应度。即使产生的代数不同,该规则也会给出相同且准确的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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