The Application of Searching Closest Distance (Travelling Salesmen Problem) Using Ant Colony Optimizatiton Algorithm (ACO) (A Case Study: PT. Nusantara Abadi)

Panji Novantara, Usep Sapari Septiana
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Abstract

Traveling from one place or city to another by considering the efficiency of time and costs requires accuracy in determining the path between the nearby cities. The results need speed and accuracy by using computers. Additionally the service application system is very supportive towards the mobility of users. This condition also happens in the distribution of mineral water. PT. Abadi Nusantara produces several sizes Nalala drinking water circulating in the community, including 19 liter gallon of bottled water, bottled water 500 milliliter, and bottled water 250 milliliter glass. The courier distributors that send the goods, have to consider the time and cost efficiencies when they conduct distribution activities, then sought the shortest distance from their distribution journey. There are various theories of algorithms that can be used to solve the problems of goods distribution or it is often called Travelling Salesmen Problem (TSP), such as brute force algorithm, greedy algorithm, ant colony algorithm, Djikstra, A *, etc. In this thesis, the author chooses one of the algorithms, it is ant colony algorithm to solve the problem of distribution.Keywords : Searching Closest Distance, Traveling Salesman Problem, Ant Colony Optimization Algorithm.
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蚁群优化算法(ACO)在搜索最近距离(旅行推销员问题)中的应用(以PT. Nusantara Abadi为例)
考虑到时间和成本的效率,从一个地方或城市旅行到另一个地方或城市需要准确地确定附近城市之间的路径。使用计算机计算结果需要速度和准确性。此外,服务应用系统非常支持用户的移动性。这种情况也发生在矿泉水的分布中。PT. Abadi Nusantara生产几种规格的纳拉拉饮用水,在社区循环使用,包括19升加仑的瓶装水,500毫升的瓶装水和250毫升的瓶装水。快递商在进行配送活动时,必须考虑时间和成本效益,然后在配送过程中寻求最短的距离。有各种各样的算法理论可以用来解决货物分配问题,或者通常被称为旅行推销员问题(TSP),如蛮力算法,贪婪算法,蚁群算法,Djikstra, A *等。在本文中,作者选择了其中的一种算法,即蚁群算法来解决分配问题。关键词:最近距离搜索,旅行商问题,蚁群优化算法。
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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