Hybrid Whale-fish swarm algorithm to optimize disassembly line balancing problem
DOI:
https://doi.org/10.54388/jkues.v2i3.205Keywords:
disassembly line balancing, artificial fish, improved artificial fish, artificial whales, hybrid whale-fish swarm algorithmAbstract
The disassembly of waste products positively affects the environment, reduces the risk of environmental pollution, reduces the risk of spreading dangerous parts to the life of living entities, and provides the continuity of life on this planet. This research aims to contribute to the aforementioned of maintaining a clean environment by developing algorithms that help solve the problems of dismantling waste products and polluting the environment. In previous literature, researchers developed algorithms for artificial fish, improved artificial fish, and artificial whales to search for solutions with higher evidence instead From the local evidence,
in this paper the artificial fish algorithm has been hybridized with the artificial whale algorithm for the purpose of developing solutions, and this method depends on the artificial fish flock first searching for food and here representing global solutions and avoiding predating traps (hooks with trap food), and after finding a flock The artificial swarm is the real food. The artificial whale sends bubbles to collect the swarm of artificial fish in the place of global high solutions, and gets them at once. The proposed algorithm was compared with the algorithms in the previous literature, and the results were as follows: Workstation was decreased by 0.5% to 22.2%, idle time was reduced by 82.5% to 84.0%, the demand index was reduced by 15% to 24.2%, and the hazardous index was reduced by 1.7% to 3.4%.
References
Y. SUN, “Quantitative evaluation method of product disassembly based on parts failure rate and disassembly time,” Journal of Mechanical Engineering, vol. 46, no. 13, p. 147, 2010. [Online]. Available: https://doi.org/10.3901%2Fjme.2010.13.147
K. Makenji, “Mechanical methods for recycling waste composites,” in Management, Recycling and Reuse of Waste Composites. Elsevier, 2010, pp. 217–252. [Online]. Available: https://doi.org/10.1533% 2F9781845697662.3.217
J. Reap and B. Bras, “Design for disassembly and the value of robotic semi-destructive disassembly,” in Volume 3: 7th Design for Manufacturing Conference. ASMEDC, jan 2002. [Online]. Available: https://doi.org/10.1115%2Fdetc2002%2Fdfm-34181
R. GENG, “NON-DISASSEMBLY DIAGNOSING THE TRANSMISSION OF a MOTOR VEHICLE — THEORY AND HARDWARE,” in Non-Destructive Testing. Elsevier, 1989, pp. 502–504. [Online]. Available: https://doi.org/10.1016%2Fb978-0-444-87450-4.50117-x
K. Wang, X. Li, and L. Gao, “Modeling and optimization of multi-objective partial disassembly line balancing problem considering hazard and profit,” Journal of Cleaner Production, vol. 211, pp.115–133, feb 2019. [Online]. Available: https://doi.org/10.1016%2Fj.clepro.2018.11.114
N. Siddig, Z. Zhang, A. Mokhtar, and A. Abualnor, “Optimizing disassembly line balancing problem using multi-objective adam optimizer algorithm,” Journal of Karary University for Engineering and Science, 2021.
A. Nadir, Z. Z. Qiang, A. Mokhtar, and A. B. Abualnor, “A hybrid genetic-adam optimizer to improve disassembly line balancing problem,” Journal of Karary University for Engineering and Science, 2022.
K. M. Bukkur, K. Ahmed, and M. Shukri, “Dynamic scheduling of assembly line using neural networks for saria industrial complex-sudan,” Journal of Karary University for Engineering and Science, 2022.
C. G. S. Sikora, Assembly-Line Balancing under Demand Uncertainty. Springer, 2022.
T. Yin, Z. Zhang, Y. Zhang, T. Wu, and W. Liang, “Mixed-integer programming model and hybrid driving algorithm for multi-product partial disassembly line balancing problem with multi-robot workstations,” Robotics and Computer-Integrated Manufacturing, vol. 73, p. 102251, 2022.
S. McGovern and S. Gupta, Disassembly Line: Balancing and Modeling. McGraw-Hill Education, 2011.
M. L. Bentaha, O. Batta¨?a, and A. Dolgui, “An exact solution approach 1for disassembly line balancing problem under uncertainty of the task processing times,” International Journal of Production Research, vol. 53, no. 6, pp. 1807–1818, 2015.
Z. Zhang, K. Wang, L. Zhu, and Y. Wang, “A pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem,” Expert Systems with Applications, vol. 86, pp. 165–176, 2017.
M. A. Ilgin, “A dematel-based disassembly line balancing heuristic,” Journal of Manufacturing Science and Engineering, vol. 141, no. 2, 2019.
S. M. McGovern and S. M. Gupta, “2-opt heuristic for the disassembly line balancing problem,” in Environmentally conscious manufacturing iii, vol. 5262. International Society for Optics and Photonics, 2004, pp. 71–84.
O. Haz?r, X. Delorme, and A. Dolgui, “A review of cost and profit ¨ oriented line design and balancing problems and solution approaches,” Annual Reviews in Control, vol. 40, pp. 14–24, 2015.
S. M. McGovern and S. M. Gupta, “A balancing method and genetic algorithm for disassembly line balancing,” European journal of operational research, vol. 179, no. 3, pp. 692–708, 2007.
S. Agrawal, Y. Dashora, M. K. Tiwari, and Y.-J. Son, “Interactive particle swarm: a pareto-adaptive metaheuristic to multiobjective optimization,” IEEE Transactions on systems, man, and cybernetics-Part A: Systems and humans, vol. 38, no. 2, pp. 258–277, 2008.
E. Tuncel, A. Zeid, and S. Kamarthi, “Solving large scale disassembly line balancing problem with uncertainty using reinforcement learning,” Journal of Intelligent Manufacturing, vol. 25, no. 4, pp. 647–659, 2014.
C. B. Kalayci and S. M. Gupta, “A tabu search algorithm for balancing a sequence-dependent disassembly line,” Production Planning & Control, vol. 25, no. 2, pp. 149–160, 2014.
F. T. Altekin, L. Kandiller, and N. E. Ozdemirel, “Profit-oriented disassembly-line balancing,” International Journal of Production Research, vol. 46, no. 10, pp. 2675–2693, 2008.
F. T. Altekin, “A comparison of piecewise linear programming formulations for stochastic disassembly line balancing,” International Journal of Production Research, vol. 55, no. 24, pp. 7412–7434, 2017.
Z. Li and M. N. Janardhanan, “Modelling and solving profit-oriented u-shaped partial disassembly line balancing problem,” Expert Systems with Applications, vol. 183, p. 115431, 2021.
L. Zhu, Z. Zhang, and C. Guan, “Multi-objective partial parallel disassembly line balancing problem using hybrid group neighbourhood search algorithm,” Journal of Manufacturing Systems, vol. 56, pp. 252–269, 2020.
Y. Zhang, Z. Zhang, C. Guan, and P. Xu, “Improved whale optimisation algorithm for two-sided disassembly line balancing problems considering part characteristic indexes,” International Journal of Production Research, vol. 60, no. 8, pp. 2553–2571, 2022.
Z. Zhang, K. Wang, L. Zhu, and Y. Wang, “A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem,” Expert Systems with Applications, vol. 86, pp. 1339–1351, 2017. [Online]. Available: http://dx.doi.org/10.1016/j.eswa. 2017.05.053
S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, may 2016.
