Solving Mixed-Model Assembly Line Balancing Problem Using a Modified Artificial Fish Swarm Algorithm

Authors

  • Fahimeh Tanhaie * Department of Industrial Engineering, Kosar university of Bojnord, Bojnord, Iran.

https://doi.org/10.48314/tsc.v1i2.44

Abstract

The Mixed-Model Assembly Line Balancing Problem (MALBP) is a critical optimization challenge in manufacturing systems, where multiple product models with similar production processes are assembled on the same line. Efficiently assigning tasks to workstations while respecting precedence constraints and cycle time limitations is essential to minimize idle time and maximize productivity. In this paper, we propose an improved Artificial Fish Swarm Algorithm (AFSA) enhanced with group escaping behavior; a natural reaction observed in fish swarms when sensing danger. This novel hybridization improves both convergence speed and global search ability of the algorithm. A detailed pseudocode of the proposed method is provided, and its performance is evaluated on several benchmark instances of MALBP. Experimental results indicate that the proposed algorithm outperforms the standard AFSA in terms of solution quality and stability, making it a promising approach for real-world mixed-model assembly line optimization.

Keywords:

Mixed-model assembly line, Balancing problem, Artificial fish swarm algorithm, Group escaping behavior

References

  1. [1] Alinaghian, M., & Naderipour, M. (2016). A novel comprehensive macroscopic model for time-dependent vehicle routing problem with multi-alternative graph to reduce fuel consumption: A case study. Computers & industrial engineering, 99, 210–222. https://doi.org/10.1016/j.cie.2016.07.029
  2. [2] Naderipour, M., & Alinaghian, M. (2016). Measurement, evaluation and minimization of CO2, NOx, and CO emissions in the open time dependent vehicle routing problem. Measurement, 90, 443–452. https://doi.org/10.1016/j.measurement.2016.04.043
  3. [3] Tanhaie, F., Rabbani, M., & Manavizadeh, N. (2021). Sequencing mixed-model assembly lines with demand management: Problem development and efficient multi-objective algorithms. Engineering optimization, 53(7), 1101–1118. https://doi.org/10.1080/0305215X.2020.1759580
  4. [4] Tanhaie, F., Rabbani, M., & Manavizadeh, N. (2020). Applying Available-To-Promise (ATP) concept in mixed-model assembly line sequencing problems in a make-to-order (MTO) environment: Problem extension, model formulation and Lagrangian relaxation algorithm. Opsearch, 57(2), 320–346. https://doi.org/10.1007/s12597-019-00436-6%0A%0A
  5. [5] Li, X. (2003). A new intelligent optimization method-artificial fish school algorithm [Thesis]. Doctor thesis of zhejiang university. https://cir.nii.ac.jp/crid/1572261549215438080
  6. [6] Zhang, M., Shao, C., Li, M., & Sun, J. (2006). Mining classification rule with artificial fish swarm. 2006 6th world congress on intelligent control and automation, 5877–5881. IEEE https://doi.org/10.1109/WCICA.2006.1714205
  7. [7] Xiao, L. (2010). A clustering algorithm based on artificial fish school. 2010 2nd international conference on computer engineering and technology, (7), V7-766. IEEE. https://doi.org/10.1109/ICCET.2010.5485745
  8. [8] Wang, Y., Zhang, W., & Li, H. (2014). Application of artificial fish swarm algorithm in image registration. Computer modelling & new technologies, 18(12B), 510–516. http://www.cmnt.lv/upload-files/ns_39brt086 CMNT1901-203 DEF-6 CMNT-73-AG-ed-VG.PDF
  9. [9] Luo, Y., Wei, W., & xin Wang, S. (2010). Optimization of pid controller parameters based on an improved artificial fish swarm algorithm. Third international workshop on advanced computational intelligence, 328–332. IEEE. https://doi.org/10.1109/IWACI.2010.5585187
  10. [10] Gao, X. Z., Wu, Y., Zenger, K., & Huang, X. (2010). A knowledge-based artificial fish-swarm algorithm. 2010 13th IEEE international conference on computational science and engineering, 327–332. IEEE. https://doi.org/10.1109/CSE.2010.49
  11. [11] Jiang, M., & Yuan, D. (2012). Parallel artificial fish swarm algorithm. Advances in control and communication (pp. 581–589). Springer. https://doi.org/10.1007/978-3-642-26007-0_72%0A%0A
  12. [12] Chen, H., Wang, S., Li, J., & Li, Y. (2007). A hybrid of artificial fish swarm algorithm and particle swarm optimization for feedforward neural network training. International conference on intelligent systems and knowledge engineering 2007, 1025–1028. Atlantis press. https://doi.org/10.2991/iske.2007.174
  13. [13] He, S., Belacel, N., Hamam, H., & Bouslimani, Y. (2009). Fuzzy clustering with improved artificial fish swarm algorithm. 2009 international joint conference on computational sciences and optimization, (2), 317–321. IEEE. https://doi.org/10.1109/CSO.2009.367
  14. [14] Chen, Z., & Tian, X. (2010). Notice of retraction: Artificial fish-swarm algorithm with chaos and its application. 2010 second international workshop on education technology and computer science (Vol. 1, pp. 226–229). IEEE. https://doi.org/10.1109/ETCS.2010.253
  15. [15] Fernandes, E., Martins, T., Maria, A., & Rocha, A. (2009). Fish swarm intelligent algorithm for bound constrained global optimization. CMMSE 2009, 1–3. https://www.researchgate.net/publication/228418487_Fish_swarm_intelligent_algorithm_for_bound_constrained_global_optimization
  16. [16] Yazdani, D., Golyari, S., & Meybodi, M. R. (2010). A new hybrid algorithm for optimization based on artificial fish swarm algorithm and cellular learning automata. 2010 5th international symposium on telecommunications, 932–937. IEEE. https://doi.org/10.1109/ISTEL.2010.5734156
  17. [17] Parrish, J. K., Viscido, S. V, & Grunbaum, D. (2002). Self-organized fish schools: An examination of emergent properties. The biological bulletin, 202(3), 296–305. https://doi.org/10.2307/1543482
  18. [18] Wang, C. R., Zhou, C. L., & Ma, J. W. (2005). An improved artificial fish-swarm algorithm and its application in feed-forward neural networks. 2005 international conference on machine learning and cybernetics (5), 2890–2894. IEEE. https://doi.org/10.1109/ICMLC.2005.1527436
  19. [19] Luo, Y., Zhang, J., & Li, X. (2007). The optimization of pid controller parameters based on artificial fish swarm algorithm. 2007 IEEE international conference on automation and logistics, 1058–1062. IEEE. https://doi.org/10.1109/ICAL.2007.4338724

Published

2025-05-11

Issue

Vol. 1 No. 2 (2025): Transactions on Soft Computing

Section

Articles

How to Cite

Tanhaie, F. (2025). Solving Mixed-Model Assembly Line Balancing Problem Using a Modified Artificial Fish Swarm Algorithm. Transactions on Soft Computing , 1(2). https://doi.org/10.48314/tsc.v1i2.44

Similar Articles

You may also start an advanced similarity search for this article.