A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing
Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
UMP Press
2019
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/27433/ http://umpir.ump.edu.my/id/eprint/27433/ http://umpir.ump.edu.my/id/eprint/27433/1/A%20modified%20artificial%20bee%20colony%20algorithm.pdf |
| Summary: | Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were proposed to optimise this problem, the existing researches on this problem were limited to Evolutionary Algorithm (EA), Ant Colony Optimisation (ACO), and Particle Swarm Optimisation (PSO). This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB problem. The proposed algorithm adopts beewolves predatory concept from Grey Wolf Optimiser to improve the exploitation ability in Artificial Bee Colony (ABC) algorithm. The proposed MABC was tested with a set of benchmark problems. The results indicated that the MABC outperformed the comparison algorithms in 91% of the benchmark problems. Furthermore, a statistical test reported that the MABC had significant performances in 80% of the cases. |
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