An improved algorithm for flexible job shop scheduling

DOI

10.2991/mce-14.2014.92How to use a DOI?

Keywords

Improved Genetic Algorithm; Crossover probability; mutation probability; Flexible Job Shop Scheduling; Optimization

Abstract

In the traditional genetic algorithm, there are some defects such as precocious, poor stability, slow search speed etc.Summarize previous genetic algorithm, I proposed a Improved Genetic Algorithms , it combination of encoding, crossover probability, mutation probability, etc. and applied to flexible job shop scheduling.This optimization can accelerate convergence and improve search speed, and can effectively improve the stability of operations, effectively overcome premature, to find the optimal solution faster.The experimental results show this improvement more quickly than ever before to find the optimal solution of the genetic algorithm.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jindong Han
AU  - Yinghong Zhang
PY  - 2014/03
DA  - 2014/03
TI  - An improved algorithm for flexible job shop scheduling
BT  - Proceedings of the 2014 International Conference on Mechatronics, Control and Electronic Engineering
PB  - Atlantis Press
SP  - 413
EP  - 417
SN  - 1951-6851
UR  - https://doi.org/10.2991/mce-14.2014.92
DO  - 10.2991/mce-14.2014.92
ID  - Han2014/03
ER  -