优化普零货物拼箱配装的遗传算法

卜雷; 尹传忠; 蒲云

优化普零货物拼箱配装的遗传算法

1.
同济大学运输管理工程系, 上海 200092
2.
西南交通大学交通运输学院, 四川成都 610031
3.
西南交通大学研究生院, 四川成都 610031

详细信息

作者简介:
卜雷(1975-), 女, 山东蓬莱人, 同济大学讲师, 博士, 从事智能运输系统研究

中图分类号: U169
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- PDF下载量: 223
- 被引次数: 0
出版历程
- 收稿日期: 2004-01-10
- 刊出日期: 2004-12-25

Genetic algorithm for optimal arrangement of general piece goods

1.
Department of Transportation and Management Engineering, Tongji University, Shanghai 200092, China
2.
School of Traffic and Transportation Engineering, Southwest Jiaotong University, Chengdu 610031, China
3.
School of Graduate, Southwest Jiaotong University, Chengdu 610031, China

More Information

Author Bio:
BU Lei(1975-), female, PhD, 86-21-51030229, leibu305@sina.com

摘要

摘要: 应用遗传算法, 考虑货物装载重量、装载容积、优先装箱及非同时配装等约束条件, 采用适当的个体编码方法, 并构造合理的适应值函数, 优化铁路集装箱运输中的普零货物拼箱配装。结果发现以42件货物装入10t箱, 利用遗传算法得到的集装箱装载重量利用率为83 8%, 优化了装载结果, 达到了装载要求, 这说明该方法是可行的。
- 物流工程 /
- 装箱 /
- 遗传算法 /
- 优化
Abstract: In order to make good use of container's loading weight of volume in railway transportation, this paper constructed reasonable coding and fitness function by improved genetic algorithm, according to loading weight, volume and priority. It was found that 42 pieces of goods are loaded into a 10 t container, the container usage ratio of loading weight is 83.8%. Applied result shows that the algorithm is feasible.
- logistics engineering /
- loading in container /
- genetic algorithm /
- optimization

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图 1 适应函数值与进化代数之间的关系

Figure 1. Relation of average value of fitness function and evolutional generation

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表 1 各货票货物质量及外径体积

Table 1. Goods quality and volume

货票编号	a_i/t	v_i/m³	货票编号	a_i/t	v_i/m³	货票编号	a_i/t	v_i/m³
1	1.221	1.05	15	1.040	2.60	29	1.102	2.46
2	1.156	1.98	16	0.805	1.23	30	2.041	2.20
3	0.700	2.00	17	1.220	0.65	31	1.900	2.80
4	1.243	3.14	18	1.000	2.40	32	2.400	3.20
5	1.600	2.86	19	1.782	0.87	33	1.029	3.00
6	1.612	2.17	20	1.100	1.54	34	3.000	1.20
7	2.300	4.80	21	1.030	5.60	35	1.840	1.20
8	1.930	5.20	22	0.730	4.40	36	1.796	3.89
9	1.850	2.30	23	1.030	1.80	37	2.650	1.01
10	1.900	3.80	24	2.430	3.80	38	1.975	1.23
11	1.120	2.00	25	1.520	4.00	39	0.800	1.00
12	1.431	4.02	26	1.890	5.46	40	1.100	3.20
13	0.600	2.78	27	1.320	3.54	41	1.200	0.80
14	0.306	3.22	28	1.150	1.60	42	2.000	1.10

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表 2 配装结果

Table 2. Result of goods arrangement

货票编号	1	2	3	4	5	6	7	8	9	10	11	12	13	14
配装结果	1	0	0	0	0	0	0	0	1	0	0	0	0	0
货票编号	15	16	17	18	19	20	21	22	23	24	25	26	27	28
配装结果	0	0	0	1	0	0	0	0	1	0	0	0	0	1
货票编号	29	30	31	32	33	34	35	36	37	38	39	40	41	42
配装结果	0	0	0	0	1	0	0	0	0	0	0	1	0	0
目标函数值	8.380

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参考文献(9)

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