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Scheduling of Flexible Manufacturing System using Genetic Algorithm (Multiobjective): A Review

by Navnikaa Rajan, Srishti Jaiswal, Tanya Kalsi, Vijai Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 19
Year of Publication: 2014
Authors: Navnikaa Rajan, Srishti Jaiswal, Tanya Kalsi, Vijai Singh
10.5120/15102-2678

Navnikaa Rajan, Srishti Jaiswal, Tanya Kalsi, Vijai Singh . Scheduling of Flexible Manufacturing System using Genetic Algorithm (Multiobjective): A Review. International Journal of Computer Applications. 86, 19 ( January 2014), 9-15. DOI=10.5120/15102-2678

@article{ 10.5120/15102-2678,
author = { Navnikaa Rajan, Srishti Jaiswal, Tanya Kalsi, Vijai Singh },
title = { Scheduling of Flexible Manufacturing System using Genetic Algorithm (Multiobjective): A Review },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 19 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 9-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number19/15102-2678/ },
doi = { 10.5120/15102-2678 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:04:38.049867+05:30
%A Navnikaa Rajan
%A Srishti Jaiswal
%A Tanya Kalsi
%A Vijai Singh
%T Scheduling of Flexible Manufacturing System using Genetic Algorithm (Multiobjective): A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 19
%P 9-15
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A flexible, integrated, computer-controlled environment allows the system to react on occurrence of changes, whether predicted or unpredicted. Scheduling machines of varying capabilities in such an environment has always been a difficult task. This work reviews the various approaches applied to the scheduling problem in an FMS. Various genetic algorithm based approaches considering varied objectives and constraints have been studied and analysed to result in a comparative study. For achieving the desired performance in an FMS it is required that a good scheduling system, taking into account the system conditions should generate an optimal schedule at the right time. Genetic algorithm is capable of finding near to optimal solution in a short time although it doesn't guarantee to find an optimal solution.

References
  1. S. Kapoor et al Improved Solution to Job Shop Scheduling Problem with Delay constraints using Genetic Algorithm International Journal of Computer Applications (0975 – 8887) Volume 45– No. 13, May 2012
  2. V. Singh et al GA based Scheduling of FMS using Roulette Wheel Selection Process
  3. M. Hyder et al A hybrid GA for simultaneously scheduling an FMC under multiple objectives
  4. Liang Sun et al Solving Job Shop Scheduling Problem Using Genetic Algorithm with Penalty Function
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  7. Nidhish Mathew Nidhish Evaluation of Genetic Algorithm Approach for Scheduling Optimization of Flexible Manufacturing Systems
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  9. Jian-Bo Yang GA-Based Discrete Dynamic Programming Approach for Scheduling in FMS Environments
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  25. S. N. Sivanandam & S. N. Deepa (2008). Principles of Soft Computing.
Index Terms

Computer Science
Information Sciences

Keywords

Scheduling Flexible

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