Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA
A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as o...
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| Language: | en |
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Hindawi Publishing Corporation
2014
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| Online Access: | http://umpir.ump.edu.my/id/eprint/6089/1/Fuzzy_Mixed_Assembly_Line_Sequencing_and_Scheduling_Optimization_Model_Using_Multiobjective_Dynamic_Fuzzy_GA.pdf http://umpir.ump.edu.my/id/eprint/6089/ http://dx.doi.org/10.1155/2014/505207 |
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| author | Zahari, Taha Farzad, Tahriri Siti Zawiah, Md Dawal |
| author_facet | Zahari, Taha Farzad, Tahriri Siti Zawiah, Md Dawal |
| author_sort | Zahari, Taha |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. |
| format | Article |
| id | my.ump.umpir.6089 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2014 |
| publisher | Hindawi Publishing Corporation |
| record_format | eprints |
| spelling | my.ump.umpir.60892018-01-22T06:32:30Z http://umpir.ump.edu.my/id/eprint/6089/ Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA Zahari, Taha Farzad, Tahriri Siti Zawiah, Md Dawal TJ Mechanical engineering and machinery A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. Hindawi Publishing Corporation 2014 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/6089/1/Fuzzy_Mixed_Assembly_Line_Sequencing_and_Scheduling_Optimization_Model_Using_Multiobjective_Dynamic_Fuzzy_GA.pdf Zahari, Taha and Farzad, Tahriri and Siti Zawiah, Md Dawal (2014) Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA. The Scientific World Journal, 2014. pp. 1-20. ISSN 2356-6140 (print); 1537-744X (online). (Published) http://dx.doi.org/10.1155/2014/505207 DOI: 10.1155/2014/505207 |
| spellingShingle | TJ Mechanical engineering and machinery Zahari, Taha Farzad, Tahriri Siti Zawiah, Md Dawal Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA |
| title | Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA |
| title_full | Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA |
| title_fullStr | Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA |
| title_full_unstemmed | Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA |
| title_short | Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA |
| title_sort | fuzzy mixed assembly line sequencing and scheduling optimization model using multiobjective dynamic fuzzy ga |
| topic | TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/6089/1/Fuzzy_Mixed_Assembly_Line_Sequencing_and_Scheduling_Optimization_Model_Using_Multiobjective_Dynamic_Fuzzy_GA.pdf http://umpir.ump.edu.my/id/eprint/6089/ http://dx.doi.org/10.1155/2014/505207 |
| url_provider | http://umpir.ump.edu.my/ |