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Using micro genetic algorithm for solving scheduling problems

Job Shop Scheduling Problem (JSSP) and Timetable scheduling are known to be computationally NP–hard problems. There have been many attempts by many researchers to develop reliable scheduling software, however, many of these software have only been tested or applied on an experimental basis or on a s...

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Main Author: Tay, Cheng San
Format: Thesis
Language:English
Published: 2005
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Online Access:http://eprints.utm.my/id/eprint/35024/1/TayChengSan%20MFKE2005.pdf
http://eprints.utm.my/id/eprint/35024/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:61288?queryType=vitalDismax&query=Using+micro+genetic+algorithm+for+solving+scheduling+problems&public=true
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spelling my.utm.350242017-10-11T06:46:54Z http://eprints.utm.my/id/eprint/35024/ Using micro genetic algorithm for solving scheduling problems Tay, Cheng San Unspecified Job Shop Scheduling Problem (JSSP) and Timetable scheduling are known to be computationally NP–hard problems. There have been many attempts by many researchers to develop reliable scheduling software, however, many of these software have only been tested or applied on an experimental basis or on a small population with minimal constraints. However in actual model JSSP, the constraints involved are more complicated compared to classical JSSP and feasible schedule must be suggested within a short period of time. In this thesis, an enhanced micro GA, namely micro GA with local search is proposed to solve an actual model JSSP. The scheduler is able to generate an output of a set of feasible production plan not only at a faster rate but which can generate a plan which can reduce the makespan as compare to those using manual. Also, in this thesis, the micro GA is applied to the timetabling problem of Faculty of Electrical Engineering Universiti Teknologi Malaysia which has more than 3,000 students. Apart from having more students, the faculty also offers various different type s of specialized courses. Various constraints such as elective subjects, classrooms capacity, multiple sections students, lecturer, etc have to be taken into consideration when designing the solution for this problem. In this thesis , an enhanced micro GA is proposed for timetable scheduling in the Faculty to overcome the problems. The enhanced micro GA algorithm is referred to as distributed micro GA which has local search to speed up the scheduling process. Comparisons are made with simple GA methods such that a more optimal solution can be achieved. The proposed algorithm is successfully implemented at the Faculty meeting a variety of constraints not achievable using manual methods. 2005 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/35024/1/TayChengSan%20MFKE2005.pdf Tay, Cheng San (2005) Using micro genetic algorithm for solving scheduling problems. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:61288?queryType=vitalDismax&query=Using+micro+genetic+algorithm+for+solving+scheduling+problems&public=true
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic Unspecified
spellingShingle Unspecified
Tay, Cheng San
Using micro genetic algorithm for solving scheduling problems
description Job Shop Scheduling Problem (JSSP) and Timetable scheduling are known to be computationally NP–hard problems. There have been many attempts by many researchers to develop reliable scheduling software, however, many of these software have only been tested or applied on an experimental basis or on a small population with minimal constraints. However in actual model JSSP, the constraints involved are more complicated compared to classical JSSP and feasible schedule must be suggested within a short period of time. In this thesis, an enhanced micro GA, namely micro GA with local search is proposed to solve an actual model JSSP. The scheduler is able to generate an output of a set of feasible production plan not only at a faster rate but which can generate a plan which can reduce the makespan as compare to those using manual. Also, in this thesis, the micro GA is applied to the timetabling problem of Faculty of Electrical Engineering Universiti Teknologi Malaysia which has more than 3,000 students. Apart from having more students, the faculty also offers various different type s of specialized courses. Various constraints such as elective subjects, classrooms capacity, multiple sections students, lecturer, etc have to be taken into consideration when designing the solution for this problem. In this thesis , an enhanced micro GA is proposed for timetable scheduling in the Faculty to overcome the problems. The enhanced micro GA algorithm is referred to as distributed micro GA which has local search to speed up the scheduling process. Comparisons are made with simple GA methods such that a more optimal solution can be achieved. The proposed algorithm is successfully implemented at the Faculty meeting a variety of constraints not achievable using manual methods.
format Thesis
author Tay, Cheng San
author_facet Tay, Cheng San
author_sort Tay, Cheng San
title Using micro genetic algorithm for solving scheduling problems
title_short Using micro genetic algorithm for solving scheduling problems
title_full Using micro genetic algorithm for solving scheduling problems
title_fullStr Using micro genetic algorithm for solving scheduling problems
title_full_unstemmed Using micro genetic algorithm for solving scheduling problems
title_sort using micro genetic algorithm for solving scheduling problems
publishDate 2005
url http://eprints.utm.my/id/eprint/35024/1/TayChengSan%20MFKE2005.pdf
http://eprints.utm.my/id/eprint/35024/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:61288?queryType=vitalDismax&query=Using+micro+genetic+algorithm+for+solving+scheduling+problems&public=true
_version_ 1643649742856519680
score 13.211869

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