基于改进Balance算法的车货匹配研究
发布时间：2018-07-16 12:49
【摘要】：贪婪算法(Greedy algorithm)只关注当前匹配的收益,在车货匹配的过程中有可能出现集中匹配同一车型的情况,导致匹配的效果并不理想。对Balance算法进行改进,并应用于车货匹配中,提出基于改进Balance算法的车货匹配模型(Improved Balance Vehicles and Cargos Matching Model,IBVCM)。模型引入车货匹配平衡函数定义各车型的匹配情况,并根据车货之间的匹配关系对函数进行修正,在为货物选择匹配车型时综合考虑当前车货匹配的收益以及车型匹配情况两个因素。实验结果表明,文中所提的IBVCM模型与贪婪算法相比匹配的成功率提高13.5%,匹配的总收益提高18%。
[Abstract]:Greedy algorithm only pays attention to the current matching income. In the process of vehicle and cargo matching, it is possible to focus on matching the same vehicle, which leads to the unsatisfactory matching effect. This paper improves the balance algorithm and applies it to the vehicle and cargo matching. An improved balance vehicles and cargos matching Model (IBVCM) is proposed based on the improved balance algorithm. The model introduces vehicle and cargo matching balance function to define the matching situation of each vehicle, and modifies the function according to the matching relationship between vehicles and goods. When selecting the matching model for the goods, two factors are considered synthetically: the income of the current vehicle and cargo matching and the matching situation of the vehicle type. The experimental results show that the IBVCM model proposed in this paper increases the success rate of matching by 13.5% and the total income of matching by 18% compared with greedy algorithm.
【作者单位】： 华南师范大学经济与管理学院;
【基金】：广东省科技厅软科学研究计划(2014A07073043) 2016年广州市产学研协同创新重大专项
【分类号】：F252
本文编号：2126440
[Abstract]:Greedy algorithm only pays attention to the current matching income. In the process of vehicle and cargo matching, it is possible to focus on matching the same vehicle, which leads to the unsatisfactory matching effect. This paper improves the balance algorithm and applies it to the vehicle and cargo matching. An improved balance vehicles and cargos matching Model (IBVCM) is proposed based on the improved balance algorithm. The model introduces vehicle and cargo matching balance function to define the matching situation of each vehicle, and modifies the function according to the matching relationship between vehicles and goods. When selecting the matching model for the goods, two factors are considered synthetically: the income of the current vehicle and cargo matching and the matching situation of the vehicle type. The experimental results show that the IBVCM model proposed in this paper increases the success rate of matching by 13.5% and the total income of matching by 18% compared with greedy algorithm.
【作者单位】： 华南师范大学经济与管理学院;
【基金】：广东省科技厅软科学研究计划(2014A07073043) 2016年广州市产学研协同创新重大专项
【分类号】：F252
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