[1]郑爱萍,金福江.多产品运输问题的建模及优化算法设计[J].华侨大学学报(自然科学版),2013,34(3):281-285.[doi:10.11830/ISSN.1000-5013.2013.03.0281]
　ZHENG Ai-ping,JIN Fu-jiang.Modeling and Optimization Algorithm Design of Transportation Problem for Multiple Products[J].Journal of Huaqiao University(Natural Science),2013,34(3):281-285.[doi:10.11830/ISSN.1000-5013.2013.03.0281]
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多产品运输问题的建模及优化算法设计()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第34卷
期数:
2013年第3期
页码:
281-285
栏目:
出版日期:
2013-05-20

文章信息/Info

Title:
Modeling and Optimization Algorithm Design of Transportation Problem for Multiple Products
文章编号:
1000-5013(2013)03-0281-05
作者:
郑爱萍;  金福江
华侨大学 信息科学与工程学院, 福建 厦门 361021
Author(s):
ZHENG Ai-ping;  JIN Fu-jiang
College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
关键词:
物流;  运输问题;  遗传算法;  内点算法
Keywords:
logistics;  transportation problems;  genetic algorithm;  interior point algorithm
分类号:
O212;F252
DOI:
10.11830/ISSN.1000-5013.2013.03.0281
文献标志码:
A
摘要:
以某纺织企业的产品运输流程和企业生产、销售对产品运输的具体需求为例,建立以总运输费用最低为目标函数,以每个生产地每种产品的生产量、每个销售地每种产品的销售量,以及每种产品的单位运价为约束条件的多种产品运输模型.设计具有全局优化、收敛速度快的遗传算法,并对该模型进行优化求解.通过与传统算法的比较,说明采用遗传算法求出的运输总费用优于用内点算法计算出的结果,即对于大规模的多产品运输问题,采用遗传算法优化性能更好,不易陷入局部最优,且其收敛速度也优于内点算法.
Abstract:
Taking a textile enterprise product transportation process, and specific needs of product transportation about enterprise production and sales for example, builting transportation model of multiple products, in which the object function is to minimize the total transportation cost, and the constraint condition is the production of each product in each producer and the sales volume of product in each seller as well as the unit transportation rate of each product. Designing genetic algorithm with global optimization and the convergence speed, and use it to obtain the optimization solution of the model.Compared with the traditional algorithm, the result shows that the total transportation cost figured out by using the genetic algorithm is better than that by using interior point algorithm. That is, for large-scale multiple product transportation problems, the genetic algorithm has better optimization performance, and is not easy to fall into local optimum, and the convergence speed is also superior to the interior point algorithm.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2012-06-15
通信作者: 金福江(1965-),男,教授,主要从事复杂系统建模、仿真与控制的研究.E-mail:jinfujiang@163.com.
基金项目: 国家自然科学基金资助项目(61143005)
更新日期/Last Update: 2013-05-20