Multi-Objective Discrete Particle Swarm Optimisation Algorithm for Integrated Assembly Sequence Planning and Assembly Line Balancing
In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. A...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institution of Mechanical Engineers
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/15059/ http://umpir.ump.edu.my/id/eprint/15059/ http://umpir.ump.edu.my/id/eprint/15059/ http://umpir.ump.edu.my/id/eprint/15059/1/IMechE%20Part%20B-%20Journal%20of%20Engineering%20Manufacture-2016-Ab%20Rashid-0954405416673095.pdf |
| Summary: | In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set. |
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