Integrated process planning and scheduling using genetic algorithms
Process planning and scheduling are two of the most important functions in any manufacturing system. Traditionally process planning and scheduling are considered as two separate functions. In this paper a Genetic Algorithm (GA) for integrated process planning and scheduling is proposed where selecti...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2017-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/277458 |
| _version_ | 1827283043484696576 |
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| author | Imran Ali Chaudhry Muhammad Usman |
| author_facet | Imran Ali Chaudhry Muhammad Usman |
| author_sort | Imran Ali Chaudhry |
| collection | DOAJ |
| description | Process planning and scheduling are two of the most important functions in any manufacturing system. Traditionally process planning and scheduling are considered as two separate functions. In this paper a Genetic Algorithm (GA) for integrated process planning and scheduling is proposed where selection of the best process plan and scheduling of jobs in a job shop environment are done simultaneously. In the proposed approach a domain independent spreadsheet based approach is presented to solve this class of problems. The precedence relations among job operations are considered in the model, based on which implicit representation of a feasible process plans for each job can be done. To verify the performance and feasibility of the presented approach, the proposed algorithm has been evaluated against a number of benchmark problems that have been adapted from the previously published literature. The experimental results show that the proposed approach can efficiently achieve optimal or near-optimal solutions for the problems adopted from literature. It is also demonstrated that the proposed algorithm is of general purpose in application and could be used for the optimisation of any objective function without changing the model or the basic GA routine. |
| first_indexed | 2024-04-24T09:29:12Z |
| format | Article |
| id | doaj.art-b9df153c85a64a889d620f1d928c2ae5 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:29:12Z |
| publishDate | 2017-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-b9df153c85a64a889d620f1d928c2ae52024-04-15T14:25:59ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392017-01-012451401140910.17559/TV-20151121212910Integrated process planning and scheduling using genetic algorithmsImran Ali Chaudhry0Muhammad Usman1Department of Industrial Engineering, College of Engineering, University of Hail, Ha’il, Saudi ArabiaDepartment of Industrial Engineering, College of Engineering, University of Hail, Ha’il, Saudi ArabiaProcess planning and scheduling are two of the most important functions in any manufacturing system. Traditionally process planning and scheduling are considered as two separate functions. In this paper a Genetic Algorithm (GA) for integrated process planning and scheduling is proposed where selection of the best process plan and scheduling of jobs in a job shop environment are done simultaneously. In the proposed approach a domain independent spreadsheet based approach is presented to solve this class of problems. The precedence relations among job operations are considered in the model, based on which implicit representation of a feasible process plans for each job can be done. To verify the performance and feasibility of the presented approach, the proposed algorithm has been evaluated against a number of benchmark problems that have been adapted from the previously published literature. The experimental results show that the proposed approach can efficiently achieve optimal or near-optimal solutions for the problems adopted from literature. It is also demonstrated that the proposed algorithm is of general purpose in application and could be used for the optimisation of any objective function without changing the model or the basic GA routine.https://hrcak.srce.hr/file/277458integrated process planning and scheduling (IPPS)genetic algorithmsjob shop |
| spellingShingle | Imran Ali Chaudhry Muhammad Usman Integrated process planning and scheduling using genetic algorithms Tehnički Vjesnik integrated process planning and scheduling (IPPS) genetic algorithms job shop |
| title | Integrated process planning and scheduling using genetic algorithms |
| title_full | Integrated process planning and scheduling using genetic algorithms |
| title_fullStr | Integrated process planning and scheduling using genetic algorithms |
| title_full_unstemmed | Integrated process planning and scheduling using genetic algorithms |
| title_short | Integrated process planning and scheduling using genetic algorithms |
| title_sort | integrated process planning and scheduling using genetic algorithms |
| topic | integrated process planning and scheduling (IPPS) genetic algorithms job shop |
| url | https://hrcak.srce.hr/file/277458 |
| work_keys_str_mv | AT imranalichaudhry integratedprocessplanningandschedulingusinggeneticalgorithms AT muhammadusman integratedprocessplanningandschedulingusinggeneticalgorithms |