.png){kind=icon}
.png){kind=icon}
Return
Improving benders decomposition using a genetic algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2008.10.033.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We develop and investigate the performance of a hybrid solution framework for solving mixed-integer linear programming problems. Benders decomposition and a genetic algorithm are combined to develop a framework to Compute feasible solutions. We decompose the problem into a master problem and a subproblem. A genetic algorithm along with a heuristic are used to obtain feasible solutions to the master problem, whereas the subproblem is solved to optimality using a linear programming solver. Over successive iterations the master problem is refined by adding cutting planes that are implied by the subproblem. We compare the performance of the approach against a standard Blenders decomposition approach as well as against a stand-alone solver (Cplex) on MIPLIB test problems. (C) 2008 Elsevier B.V. All rights reserved.
Keywords:
Genetic algorithm
Benders decomposition
Mixed-integer linear programs
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W