.png){kind=icon}
返回
Variable neighborhood search for quadratic multiple constraint variable sized bin-packing problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cor.2022.105803.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The bin-packing problem is well known in the field of combinatorial optimization. In this paper, we propose a quadratic multiple constraint variable sized bin-packing problem (QMC-VSBPP), which is a generalization of the variable sized bin-packing problem (VSBPP). In QMC-VSBPP, each bin type has multiple capacities, each item has multiple weights, and the items in different bins have joint costs. The objective of QMC-VSBPP is to minimize the sum of costs of used bins and the joint costs of items in different bins. QMC-VSBPP is a new combinatorial optimization problem. Herein, a variable neighborhood search (VNS) heuristic is proposed to solve QMC-VSBPP. The proposed VNS uses a special hierarchical clustering algorithm to generate the initial solution, in which three neighborhood operators are presented to expand solution space, and a local search algorithm based on a combination of two local operators is presented to find a local optimal solution. To evaluate the performance of the proposed VNS for QMC-VSBPP, a genetic algorithm and a variable neighborhood search for VSBPP were adapted for the state-of-the-art QMC-VSBPP, and the commercial solver CPLEX was also applied to QMC-VSBPP. Results of computational experiments on 96 test instances show that the proposed VNS could yield better solutions than those of the adapted genetic algorithm (GA), the adapted VNS, and CPLEX.
Keyword:
Combinatorial optimization
Quadratic multiple constraint variable sized bin-packing problem
Variable neighborhood search
Hierarchical clustering
期刊
C
IF:
4.3
论文数:
6.5K
被引数:
1.8W
机构
引用论文
.png){kind=icon}
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏
查看更多内容