Solving Container Loading Problem with Genetic Algorithm

被引:0
作者
Erdem, Huseyin Askin [1 ]
机构
[1] Turkish Air Force Acad, ASTIN, Dept Comp Engn, Yesilyurt Istanbul, Turkey
来源
2014 IEEE 15TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND INFORMATICS (CINTI) | 2014年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The aim of this study is to solve the evaluated application within the scope of the container loading problem by using a genetic algorithm. Taking into account the loading constraints, finding an optimal result that contains an acceptable solution was investigated with the help of maximization of goods to be loaded to the container. By using an evolutionary algorithm (genetic algorithm) in the study, an optimal solution is investigated by producing new generations according to "the best survivals" principle. the result was tried to be found by taking into consideration the fitness values specified for the produced new generations. In the study, how the goods of the logistically used companies are loaded into containers is evaluated. Also, as a target(aim) function, to decrease transport cost of the company was argued by specifying the maximum load to be carried by a single container.
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收藏
页码:391 / 396
页数:6
相关论文
共 18 条
[11]  
Mitchell M., 1995, COMPLEXITY, V1
[12]  
Mouraa A., 2005, IEEE INTELLIGENT SYS, V20
[13]  
Ocal Z., 2013, IEEE INTELL SYST APP, P525
[14]  
Özalp N, 2013, INT CONF UNMAN AIRCR, P308
[15]  
Popli R., 2014, INT J ADV RES COMPUT, V4
[16]   Generation of Bezier Curve-Based Flyable Trajectories for Multi-UAV Systems with Parallel Genetic Algorithm [J].
Sahingoz, Ozgur Koray .
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2014, 74 (1-2) :499-511
[17]  
Sahingoz OK, 2013, INT CONF UNMAN AIRCR, P41
[18]  
Sastry K., 2005, Genet. Algorithms, P97