A new multiobjective tiki-taka algorithm for optimization of assembly line balancing
Purpose: This study aims to propose a new multiobjective optimization metaheuristic based on the tiki-taka algorithm (TTA). The proposed multiobjective TTA (MOTTA) was implemented for a simple assembly line balancing type E (SALB-E), which aimed to minimize the cycle time and workstation number simu...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
Emerald Group Publishing Ltd.
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/37577/1/2023%20MOTTA%20Eng%20Comp.pdf http://umpir.ump.edu.my/id/eprint/37577/7/A%20new%20multiobjective%20tiki-taka%20algorithm%20for%20optimization%20of%20assembly%20line%20balancing.pdf http://umpir.ump.edu.my/id/eprint/37577/ https://doi.org/10.1108/EC-03-2022-0185 https://doi.org/10.1108/EC-03-2022-0185 |
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| Institution: | Universiti Malaysia Pahang |
| Language: | English English |
| Summary: | Purpose: This study aims to propose a new multiobjective optimization metaheuristic based on the tiki-taka algorithm (TTA). The proposed multiobjective TTA (MOTTA) was implemented for a simple assembly line balancing type E (SALB-E), which aimed to minimize the cycle time and workstation number simultaneously. Design/methodology/approach: TTA is a new metaheuristic inspired by the tiki-taka playing style in a football match. The TTA is previously designed for a single-objective optimization, but this study extends TTA into a multiobjective optimization. The MOTTA mimics the short passing and player movement in tiki-taka to control the game. The algorithm also utilizes unsuccessful ball pass and multiple key players to enhance the exploration. MOTTA was tested against popular CEC09 benchmark functions. Findings: The computational experiments indicated that MOTTA had better results in 82% of the cases from the CEC09 benchmark functions. In addition, MOTTA successfully found 83.3% of the Pareto optimal solution in the SALB-E optimization and showed tremendous performance in the spread and distribution indicators, which were associated with the multiple key players in the algorithm. Originality/value: MOTTA exploits the information from all players to move to a new position. The algorithm makes all solution candidates have contributions to the algorithm convergence. |
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