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Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm.

The current trend in manufacturing technology is considered by two main items automation andflexibility. Flexible manufacturing system (FMS) is one of the most identified systems that include bothautomation and flexibility criteria. It comprises three principle elements: computer controlled machine...

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Main Authors: Sulaiman, Shamsuddin, Mohd Ariffin, Mohd Khairol Anuar, Badakshian, Mostafa
Format: Article
Language:English
English
Published: 2012
Online Access:http://psasir.upm.edu.my/id/eprint/23462/7/Performance%20optimization%20of%20simultaneous%20machine%20and%20automated%20guided%20vehicle%20scheduling%20using%20fuzzy%20logic%20controller%20based%20genetic%20algorithm.pdf
http://psasir.upm.edu.my/id/eprint/23462/
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spelling my.upm.eprints.234622016-02-17T07:57:10Z http://psasir.upm.edu.my/id/eprint/23462/ Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm. Sulaiman, Shamsuddin Mohd Ariffin, Mohd Khairol Anuar Badakshian, Mostafa The current trend in manufacturing technology is considered by two main items automation andflexibility. Flexible manufacturing system (FMS) is one of the most identified systems that include bothautomation and flexibility criteria. It comprises three principle elements: computer controlled machinetools, an automated material handling system and a computer control system. One of the automatedmaterials handling equipment in FMS is automated guided vehicles (AGVs). Integrated scheduling ofAGVs and machines is an essential factor contributing to the efficiency of the manufacturing system inFMS environment. Previously, genetic algorithm (GA) is considered as a heuristic method to solve AGVscheduling problem. GA may not be able to achieve the global optimum due to premature convergencebecause of control’s lack on its operators parameters. Fuzzy logic controller (FLC) is proposed tocontrol the behavior of GA during solving the scheduling problem of AGVs. This paper presents a job-based GA that is based on job sequencing. Through the optimization, the FLC is used to control the GAoperators (crossover and mutation rate) simultaneous to solve the AGV scheduling problem 2012 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/23462/7/Performance%20optimization%20of%20simultaneous%20machine%20and%20automated%20guided%20vehicle%20scheduling%20using%20fuzzy%20logic%20controller%20based%20genetic%20algorithm.pdf Sulaiman, Shamsuddin and Mohd Ariffin, Mohd Khairol Anuar and Badakshian, Mostafa (2012) Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm. International Journal of Physical Sciences, 7 (9). pp. 1461-1471. ISSN 1992-1950 10.5897/IJPS11.407 English
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
description The current trend in manufacturing technology is considered by two main items automation andflexibility. Flexible manufacturing system (FMS) is one of the most identified systems that include bothautomation and flexibility criteria. It comprises three principle elements: computer controlled machinetools, an automated material handling system and a computer control system. One of the automatedmaterials handling equipment in FMS is automated guided vehicles (AGVs). Integrated scheduling ofAGVs and machines is an essential factor contributing to the efficiency of the manufacturing system inFMS environment. Previously, genetic algorithm (GA) is considered as a heuristic method to solve AGVscheduling problem. GA may not be able to achieve the global optimum due to premature convergencebecause of control’s lack on its operators parameters. Fuzzy logic controller (FLC) is proposed tocontrol the behavior of GA during solving the scheduling problem of AGVs. This paper presents a job-based GA that is based on job sequencing. Through the optimization, the FLC is used to control the GAoperators (crossover and mutation rate) simultaneous to solve the AGV scheduling problem
format Article
author Sulaiman, Shamsuddin
Mohd Ariffin, Mohd Khairol Anuar
Badakshian, Mostafa
spellingShingle Sulaiman, Shamsuddin
Mohd Ariffin, Mohd Khairol Anuar
Badakshian, Mostafa
Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm.
author_facet Sulaiman, Shamsuddin
Mohd Ariffin, Mohd Khairol Anuar
Badakshian, Mostafa
author_sort Sulaiman, Shamsuddin
title Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm.
title_short Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm.
title_full Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm.
title_fullStr Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm.
title_full_unstemmed Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm.
title_sort performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm.
publishDate 2012
url http://psasir.upm.edu.my/id/eprint/23462/7/Performance%20optimization%20of%20simultaneous%20machine%20and%20automated%20guided%20vehicle%20scheduling%20using%20fuzzy%20logic%20controller%20based%20genetic%20algorithm.pdf
http://psasir.upm.edu.my/id/eprint/23462/
_version_ 1643828064403062784
score 13.211869

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