Optimization of mixed-model assembly line balancing problem with resource constraints
In this study, mixed-model assembly line balanuinfi problem is used to- analyze the performance of four evolutionary algorithms (E'As), namely particle swarm optimization (PSO), simulated annealing (SA), ant colony optimization (ACO) and genetic algorithm (GA). Three categories of test problem...
| Main Authors: | , , |
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| Format: | Book Section |
| Language: | English |
| Published: |
Centre for Advanced Research on Energy (CARe)
2017
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/18289/ http://umpir.ump.edu.my/id/eprint/18289/ http://umpir.ump.edu.my/id/eprint/18289/1/5.Optimization%20of%20mixed-model%20assembly%20line%20balancing%20problem%20with%20resource%20constraints.pdf |
| Summary: | In this study, mixed-model assembly line balanuinfi problem is used to- analyze the performance of four evolutionary algorithms (E'As), namely particle swarm optimization (PSO), simulated annealing (SA), ant colony optimization (ACO) and genetic algorithm (GA). Three categories of test problem (small, medium, and large) is used ranging from 8 to 100 number of tasks. For computational experiment, MATLAB software is used in investigate the EAs performance to optimize the designated objective function. The results reveal that ACO performed hetter in lerm of solution quality of fitness function However, in term of processing time, PSO was the fastest followed by ACO. GA, and SA. |
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