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首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >A HYBRID OPTIMIZATION METHOD FOR VEHICLE ROUTING PROBLEM USING ARTIFICIAL BEE COLONY AND GENETIC ALGORITHM
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A HYBRID OPTIMIZATION METHOD FOR VEHICLE ROUTING PROBLEM USING ARTIFICIAL BEE COLONY AND GENETIC ALGORITHM

机译:一种使用人工蜂殖民地和遗传算法的车辆路径问题混合优化方法

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Vehicle Routing Problem is one of the classic problems in GIS (Geospatial Information System) which had been studied for long times. An answer can be accepted as a good solution if it would be able to optimize the total length of the route or decrease the number of vehicles. A VRP defines finding the optimum route for some vehicles that serve to some customers and return to the service center. This problem is economically important because the cost and the time of serving to costumers are related to optimization of the problem’s answer. Furthermore, there are many problems like BUS management, Post pickup and delivery system and other servicing systems, which are technically similar to VRP. The aim of these problems is finding a composition of optimum routes between server and costumers. In addition, as the cost is related to time, finding shortest path means decreasing cost serving and decreasing time. In this article, a hybrid model using Artificial Bee Colony and Genetic Algorithm is proposed to solve VRP. In the first step, Artificial Bee Colony has been used to find a solution for five vehicles. The scout and the onlooker bees produced in 8 modes by two methods including the nearest neighborhood and the wide neighborhood. In the second step, the Genetic Algorithm helps to optimize the solutions. The results show that the production of the scout bees is the most effective factor in the answers to the problem and helps greatly converging the answers as soon as possible.
机译:车辆路由问题是GIS(地理空间信息系统)的经典问题之一,已经很长时间研究。如果能够优化路线的总长度或减少车辆数量,则可以接受答案作为良好解决方案。 VRP定义为某些用于某些客户提供服务并返回服务中心的最佳路线。这个问题在经济上是重要的,因为为顾客服务的成本和时间与解决问题的优化有关。此外,总线管理,拾取后拾取和交付系统和其他服务系统等问题存在许多问题,这些服务系统在技术上与VRP类似。这些问题的目的正在寻找服务器和客户之间的最佳路线的组成。另外,由于成本与时间有关,找到最短路径意味着降低成本和减少时间。在本文中,提出了一种使用人造群菌落和遗传算法的混合模型来解决VRP。在第一步中,人造蜜蜂殖民地已被用来找到五辆车的解决方案。侦察员和旁观者蜜蜂在8种模式下生产的两种方法,包括最近的邻居和广阔的邻居。在第二步中,遗传算法有助于优化解决方案。结果表明,Scout蜜蜂的生产是问题答案中最有效的因素,并有助于尽快促成答案。

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