Enhanced simulated annealing for solving aggregate production planning
Simulated annealing () has been an effective means that can address difficulties related to optimisation problems. is now a common discipline for research with several productive applications such as production planning. Due to the fact that aggregate production planning () is one of the most consid...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi Publishing Corporation
2016
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/54165/1/Enhanced%20simulated%20annealing%20for%20solving%20aggregate%20production%20planning.pdf |
| _version_ | 1796976026281050112 |
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| author | Abu Bakar, Mohd Rizam Bakheet, Abdul Jabbar Khudhur Kamil, Farah Kalaf, Atiya Abbas, Iraq T. Lee, Lai Soon |
| author_facet | Abu Bakar, Mohd Rizam Bakheet, Abdul Jabbar Khudhur Kamil, Farah Kalaf, Atiya Abbas, Iraq T. Lee, Lai Soon |
| author_sort | Abu Bakar, Mohd Rizam |
| collection | UPM |
| description | Simulated annealing () has been an effective means that can address difficulties related to optimisation problems. is now a common discipline for research with several productive applications such as production planning. Due to the fact that aggregate production planning () is one of the most considerable problems in production planning, in this paper, we present multiobjective linear programming model for APP and optimised by . During the course of optimising for the APP problem, it uncovered that the capability of was inadequate and its performance was substandard, particularly for a sizable controlled problem with many decision variables and plenty of constraints. Since this algorithm works sequentially then the current state will generate only one in next state that will make the search slower and the drawback is that the search may fall in local minimum which represents the best solution in only part of the solution space. In order to enhance its performance and alleviate the deficiencies in the problem solving, a modified () is proposed. We attempt to augment the search space by starting with solutions, instead of one solution. To analyse and investigate the operations of the MSA with the standard and harmony search (), the real performance of an industrial company and simulation are made for evaluation. The results show that, compared to and , offers better quality solutions with regard to convergence and accuracy. |
| first_indexed | 2024-03-06T09:19:52Z |
| format | Article |
| id | upm.eprints-54165 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:19:52Z |
| publishDate | 2016 |
| publisher | Hindawi Publishing Corporation |
| record_format | dspace |
| spelling | upm.eprints-541652018-03-01T09:01:39Z http://psasir.upm.edu.my/id/eprint/54165/ Enhanced simulated annealing for solving aggregate production planning Abu Bakar, Mohd Rizam Bakheet, Abdul Jabbar Khudhur Kamil, Farah Kalaf, Atiya Abbas, Iraq T. Lee, Lai Soon Simulated annealing () has been an effective means that can address difficulties related to optimisation problems. is now a common discipline for research with several productive applications such as production planning. Due to the fact that aggregate production planning () is one of the most considerable problems in production planning, in this paper, we present multiobjective linear programming model for APP and optimised by . During the course of optimising for the APP problem, it uncovered that the capability of was inadequate and its performance was substandard, particularly for a sizable controlled problem with many decision variables and plenty of constraints. Since this algorithm works sequentially then the current state will generate only one in next state that will make the search slower and the drawback is that the search may fall in local minimum which represents the best solution in only part of the solution space. In order to enhance its performance and alleviate the deficiencies in the problem solving, a modified () is proposed. We attempt to augment the search space by starting with solutions, instead of one solution. To analyse and investigate the operations of the MSA with the standard and harmony search (), the real performance of an industrial company and simulation are made for evaluation. The results show that, compared to and , offers better quality solutions with regard to convergence and accuracy. Hindawi Publishing Corporation 2016 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/54165/1/Enhanced%20simulated%20annealing%20for%20solving%20aggregate%20production%20planning.pdf Abu Bakar, Mohd Rizam and Bakheet, Abdul Jabbar Khudhur and Kamil, Farah and Kalaf, Atiya and Abbas, Iraq T. and Lee, Lai Soon (2016) Enhanced simulated annealing for solving aggregate production planning. Mathematical Problems in Engineering, 2016. art. no. 1679315. pp. 1-9. ISSN 1024-123X; ESSN: 1563-5147 https://www.hindawi.com/journals/mpe/2016/1679315/abs/ Simulated annealing; Solving aggregate production planning; Solving aggregate production 10.1155/2016/1679315 |
| spellingShingle | Simulated annealing; Solving aggregate production planning; Solving aggregate production Abu Bakar, Mohd Rizam Bakheet, Abdul Jabbar Khudhur Kamil, Farah Kalaf, Atiya Abbas, Iraq T. Lee, Lai Soon Enhanced simulated annealing for solving aggregate production planning |
| title | Enhanced simulated annealing for solving aggregate production planning |
| title_full | Enhanced simulated annealing for solving aggregate production planning |
| title_fullStr | Enhanced simulated annealing for solving aggregate production planning |
| title_full_unstemmed | Enhanced simulated annealing for solving aggregate production planning |
| title_short | Enhanced simulated annealing for solving aggregate production planning |
| title_sort | enhanced simulated annealing for solving aggregate production planning |
| topic | Simulated annealing; Solving aggregate production planning; Solving aggregate production |
| url | http://psasir.upm.edu.my/id/eprint/54165/1/Enhanced%20simulated%20annealing%20for%20solving%20aggregate%20production%20planning.pdf |
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