.png){kind=icon}
返回
Adaptive Kernel Search: A heuristic for solving Mixed Integer linear Programs
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2017.06.005.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We introduce Adaptive Kernel Search (AKS), a heuristic framework for the solution of (general) Mixed Integer linear Programs (MIPs). AKS extends and enhances Kernel Search, a heuristic framework that has been shown to produce high-quality solutions for a number of specific (combinatorial) optimization problems in a short amount of time. AKS solves a sequence of carefully constructed restricted MIPs (using a commercial MIP solver). The computational effort required to solve the first restricted MIP guides the construction of the subsequent MIPs. The restricted MIPs are constructed around a kernel, which contains the variables that are presumably non-zero in an optimal solution. Computational results, for a set of 137 instances, show that AKS significantly outperforms other state-of-the-art heuristics for solving MIPs. AKS also compares favorably to CPLEX and offers more flexibility to trade-off solution quality and computing time. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Mixed integer linear programming
General-purpose heuristic
Kernel Search
Adaptive heuristic
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
IF:
6
论文数:
2.2W
被引数:
6.4W
机构
引用论文
Recovery of salinity gradient energy in desalination plants by reverse electrodialysis.png)
Desalination
OAAI
DesalinationIF0
OAAI分享收藏A heuristic for BILP problems: The Single Source Capacitated Facility Location ProblemBILP问题的启发式方法: 单源容量设施选址问题
PREAI
分享收藏
查看更多内容