唧唧堂编辑二朵2014-03-28 12:03 PM

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论文摘要:

中国作为一个农业大国,其农资物流的发展对于发挥农业在经济中的重要作用有着举足轻重的作用。物流成本的降低对于促进农业经济发展起到重要作用,而为了降低物流运输中的成本需要有效规划物流的运输路径,本论文就是在旅行商问题的数学模型下通过实现遗传算法来解决路径规划问题,并通过将算法实现结合地图显示功能给用户提供完整的物流配送路线规划系统。 遗传算法是一种启发于自然生物基因遗传的只能优化算法,通过对种群个体中基因序列的选择、交换、变异方式来对整个种群产生定向的变化,本论文主要的研究工作和贡献如下:
第一,通过借助Google Maps提供的服务帮助用户完成对单地点信息查询,双地点路径查询的功能,此外,用户在路径查询过程中还可以查询时事路况信息和实景地图。
第二,通过选择适当的基因选择策略、基因变异策略、基因交叉策略和一系列遗传算法参数,针对路径规划的具体问题实现遗传算法,获得良好的实际效果。
第三,将地图界面、Google Maps Server服务、遗传算法进行整合,实现用户输入多目的地地点后,Google Maps Server进行两点间的地理数据分析,将数据回传给系统遗传算法模块,遗传算法通过对数据进行分析、计算得出一条最优路径序列,然后将路径序列再次回传给Google Maps Server,从而在地图上显示出最优路径序列。
本论文将算法研究和实际应用相结合,在实际应用中提高用户的物流配送效率。其适用配送目的地限制在10个以内。

Abstract

The logistical development of agricultural goods and materials in China, which is known as a large agricultural country, played an important role in the agricultural field of economy. The reduction of logistical costs largely stimulated the development of the agricultural economy. In order to reduce the cost of logistics transportation the effective planning of the logistics transportation paths was quite necessary and important.

In this paper, we used the mathematical model of the traveling salesman problem (TSP) by implementing genetic algorithms to resolve the path planning problem and with the application of this algorithm, integrated with a map display function, we provided users with a complete logistical picture and route path planning system. 

Genetic algorithm was an intelligent optimization algorithm which is derived from biology genetics. It made a directional change in the entire population by means of selection, exchange and variation of gene sequences.

The main content and contributions of the study were as follows: 

Firstly, with the help of Google Maps services, we provided our customers with both information inquiry to a single location and a path query to dual location services.

Secondly, users could solve specific path query problems with genetic algorithms and effectively gained actual results through appropriate strategies of gene selection, mutation and genetic crossover as well as a series of genetic algorithm parameters.

In addition, the combination of the map interface Google Maps Server service and the genetic algorithm made it possible to input multiple-destination locations for users. The Google Maps Server conducted geographic data analysis between two points and sent back data to the systems genetic algorithm module. Following the data analysis then genetic algorithms calculated an optimal path sequence and returned it to the Google Maps Server which finally resulted in showing the optimal path sequence on the map. 

In this study, our successful connection of research and practical application of this algorithm largely satisfied our customers with dramatically improved efficiency of logistics and distribution. It was recommended that the ideal delivery destinations should be controlled as fewer than 10.

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唧唧堂编辑二朵

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