Artificial immune system for static and dynamic production scheduling problems

Over many decades, a large number of complex optimization problems have brought researchers' attention to consider in-depth research on optimization. Production scheduling problem is one of the optimization problems that has been the focus of researchers since the 60s. The main problem in produ...

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Main Author: Muhamad, Ahmad Shahrizal
Format: Thesis
Language:English
Published: 2017
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Online Access:http://eprints.utm.my/id/eprint/79219/1/AhmadShahrizalMuhamadPFC2017.pdf
http://eprints.utm.my/id/eprint/79219/
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spelling my.utm.792192018-10-14T08:39:33Z http://eprints.utm.my/id/eprint/79219/ Artificial immune system for static and dynamic production scheduling problems Muhamad, Ahmad Shahrizal QA75 Electronic computers. Computer science Over many decades, a large number of complex optimization problems have brought researchers' attention to consider in-depth research on optimization. Production scheduling problem is one of the optimization problems that has been the focus of researchers since the 60s. The main problem in production scheduling is to allocate the machines to perform the tasks. Job Shop Scheduling Problem (JSSP) and Flexible Job Shop Scheduling Problem (FJSSP) are two of the areas in production scheduling problems for these machines. One of the main objectives in solving JSSP and FJSSP is to obtain the best solution with minimum total completion processing time. Thus, this thesis developed algorithms for single and hybrid methods to solve JSSP and FJSSP in static and dynamic environments. In a static environment, no change is needed for the produced solution but changes to the solution are needed. On the other hand, in a dynamic environment, there are many real time events such as random arrival of jobs or machine breakdown requiring solutions. To solve these problems for static and dynamic environments, the single and hybrid methods were introduced. Single method utilizes Artificial Immune System (AIS), whereas AIS and Variable Neighbourhood Descent (VND) are used in the hybrid method. Clonal Selection Principle (CSP) algorithm in the AIS was used in the proposed single and hybrid methods. In addition, to evaluate the significance of the proposed methods, experiments and One-Way ANOVA tests were conducted. The findings showed that the hybrid method was proven to give better performance compared to single method in producing optimized solution and reduced solution generating time. The main contribution of this thesis is the development of an algorithm used in the single and hybrid methods to solve JSSP and FJSSP in static and dynamic environment. 2017 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/79219/1/AhmadShahrizalMuhamadPFC2017.pdf Muhamad, Ahmad Shahrizal (2017) Artificial immune system for static and dynamic production scheduling problems. PhD thesis, Universiti Teknologi Malaysia, Faculty of Computing.
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 QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Muhamad, Ahmad Shahrizal
Artificial immune system for static and dynamic production scheduling problems
description Over many decades, a large number of complex optimization problems have brought researchers' attention to consider in-depth research on optimization. Production scheduling problem is one of the optimization problems that has been the focus of researchers since the 60s. The main problem in production scheduling is to allocate the machines to perform the tasks. Job Shop Scheduling Problem (JSSP) and Flexible Job Shop Scheduling Problem (FJSSP) are two of the areas in production scheduling problems for these machines. One of the main objectives in solving JSSP and FJSSP is to obtain the best solution with minimum total completion processing time. Thus, this thesis developed algorithms for single and hybrid methods to solve JSSP and FJSSP in static and dynamic environments. In a static environment, no change is needed for the produced solution but changes to the solution are needed. On the other hand, in a dynamic environment, there are many real time events such as random arrival of jobs or machine breakdown requiring solutions. To solve these problems for static and dynamic environments, the single and hybrid methods were introduced. Single method utilizes Artificial Immune System (AIS), whereas AIS and Variable Neighbourhood Descent (VND) are used in the hybrid method. Clonal Selection Principle (CSP) algorithm in the AIS was used in the proposed single and hybrid methods. In addition, to evaluate the significance of the proposed methods, experiments and One-Way ANOVA tests were conducted. The findings showed that the hybrid method was proven to give better performance compared to single method in producing optimized solution and reduced solution generating time. The main contribution of this thesis is the development of an algorithm used in the single and hybrid methods to solve JSSP and FJSSP in static and dynamic environment.
format Thesis
author Muhamad, Ahmad Shahrizal
author_facet Muhamad, Ahmad Shahrizal
author_sort Muhamad, Ahmad Shahrizal
title Artificial immune system for static and dynamic production scheduling problems
title_short Artificial immune system for static and dynamic production scheduling problems
title_full Artificial immune system for static and dynamic production scheduling problems
title_fullStr Artificial immune system for static and dynamic production scheduling problems
title_full_unstemmed Artificial immune system for static and dynamic production scheduling problems
title_sort artificial immune system for static and dynamic production scheduling problems
publishDate 2017
url http://eprints.utm.my/id/eprint/79219/1/AhmadShahrizalMuhamadPFC2017.pdf
http://eprints.utm.my/id/eprint/79219/
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score 13.164666