An Estimation of Distribution Algorithm for the 3D Bin Packing Problem with Various Bin Sizes

被引：0
|
作者：
Cai, Yaxiong [1 ]
Chen, Huaping [1 ]
Xu, Rui [1 ]
Shao, Hao [2 ]
Li, Xueping [3 ]
机构：
[1] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230026, Peoples R China
[2] Shanghai Univ Int Business & Econom, Sch WTO Res & Educ, Shanghai 200336, Peoples R China
[3] Univ Tennessee, Dept Ind & Informat Engn, Knoxville, TN 37996 USA
来源：
INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2013 | 2013年 / 8206卷
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Optimization; Bin packing problem; Estimation of distribution algorithm; Genetic algorithm;
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 3D bin packing problem (3DBPP) is a practical problem modeled from modern industry application such as container ship loading and plane cargo management. Unlike traditional bin packing problem where all bins are of the same size, this paper investigates a more general type of 3DBPP with bins of various sizes. We proposed a modified univariate marginal distribution algorithm (UMDA) for solving the problem. A packing strategy derived from a deepest bottom left packing method was employed. The modified UMDA was experimentally compared with CPLEX and a genetic algorithm (GA) approach. The experimental study showed that the proposed algorithm performed better than GA and CPLEX for large-scale instances.
引用
收藏
页码：401 / 408
页数：8
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