.png){kind=icon}
返回
Effective formulation reductions for the quadratic assignment problem
.png){kind=icon}
OA
AI
DOI：10.1016/j.cor.2010.02.001.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper we study two formulation reductions for the quadratic assignment problem (QAP). In particular we apply these reductions to the well known Adams and Johnson 121 integer linear programming formulation of the QAP. We analyze two cases: In the first case, we study the effect of constraint reduction. In the second case, we study the effect of variable reduction in the case of a sparse cost matrix. Computational experiments with a set of 30 QAPLIB instances, which range from 12 to 32 locations, are presented. The proposed reductions turned out to be very effective. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Quadratic assignment problem
Linear integer programming
Linear programming relaxation
Sparse matrix
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
4.3
论文数:
6.5K
被引数:
1.8W
机构
引用论文
A branch-and-bound algorithm for the quadratic assignment problem based on the Hungarian method基于匈牙利方法的二次分配问题的分支定界算法
PREAI
分享收藏Stability of Reference Genes for Messenger RNA Quantification by Real-Time PCR in Mouse Dextran Sodium Sulfate Experimental Colitis.png)
PLOS ONEOAAI
PLOS ONEIF0
OAAI分享收藏分享收藏分享收藏分享收藏分享收藏
查看更多内容