Solving flow shop scheduling problem using a parallel genetic algorithm

被引：17

作者：

Akhshabi, Mostafa ^{[1
]}

Haddadnia, Javad

Akhshabi, Mohammad ^{[1
]}

机构：

[1] SabzevarTarbiat Moallem Univ, Dept Elect Engn, Sabzevar, Iran

来源：

FIRST WORLD CONFERENCE ON INNOVATION AND COMPUTER SCIENCES (INSODE 2011) | 2012年 / 1卷

关键词：

flow shop scheduling problems; Parallel; genetic algorithm; QUADRATIC ASSIGNMENT PROBLEM;

D O I：

10.1016/j.protcy.2012.02.073

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The effort of searching an optimal solution for scheduling problems is important for real-world industrial applications especially for mission-time critical systems. In this paper, a parallel GA is employed to solve flow shop scheduling problems to minimize the makespan. According to our experimental results, the proposed parallel genetic algorithm (PPGA) considerably decreases the CPU time without adversely affecting the makespan.

引用

页码：351 / 355

页数：5

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