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A Mathematical Model for an Integrated Assembly Line Regarding Learning and Fatigue Effects

Published online by Cambridge University Press:  08 January 2021

Reza Eslamipoor Open the ORCID record for Reza Eslamipoor [Opens in a new window]  and
Arash Nobari
Reza Eslamipoor*
Affiliation:
Social Security Organization, Tehran, Iran
Arash Nobari
Affiliation:
Department of Industrial Engineering, Bu-Ali Sina University, Hamedan, Iran E-mail: arashnob@yahoo.com
*
*Corresponding author. E-mail: reza.eslamipoor@gmail.com
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Summary

In this paper, an integrated mathematical model for the balancing and sequencing problems of a mixed-model assembly line (MMAL) is developed. The proposed model minimizes the total overload and idleness times. For the sake of reality, the impact of operator’s learning and fatigue issues on the optimization of the assembly line balancing and sequencing problems is considered. Furthermore, it is assumed that the Japanese mechanism is used in this assembly line to deal with the overload issue. With respect to the complexity level of the proposed model, a genetic algorithm is developed to solve the model. In order to set the parameters of the developed genetic algorithm, the well-known Taguchi method is used and the efficiency of this solution method is compared with the GAMS software using several test problems with different sizes. Finally, the sensitivity of the balancing and sequencing problems to the parameters such as station length, learning rate, and fatigue rate are analyzed and the impact of changing these parameters on the model is studied.

Keywords

Mixed-model assembly lineBalancingSequencingLearning and fatigue rateGenetic algorithm
Type
Article
Information
Robotica , Volume 39 , Issue 8 , August 2021 , pp. 1434 - 1450
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Sarker, B. R. and Pan, H., “Designing a mixed-model, open-station assembly line using mixed-integer programming,” J. Operat. Res. Soc., (5), 545–558 (2001).CrossRefGoogle Scholar
Boysen, N., Fliedner, M. and Scholl, A., “Sequencing mixed-model assembly lines: Survey, classification and model critique,” Eur. J. Operat. Res. 192(2), 349–373 (2009).CrossRefGoogle Scholar
Salehi, M., Fattahi, P., Roshani, A. and Zahiri, J., “Multi-Criteria sequencing problem in mixed-model synchronous assembly lines,” Int. J. Adv. Manuf. Tech., (67), 1–11 (2013).Google Scholar
Dong, J., Zhang, L., Xiao, T. and Mao, H., “Balancing and sequencing of stochastic mixed-model assembly U-lines to minimise the expectation of work overload time,” Int. J. Prod. Res. 52(24), 7529–7548 (2014).CrossRefGoogle Scholar
Chutima, P. and Naruemitwong, W., “A Pareto bioography-based optimisation for multi-objective two-sided assembly line sequencing problems with a learning effect,” Comput. Ind. Eng. 69, 89–104 (2014).CrossRefGoogle Scholar
Wright, T. P., “Factors affecting the cost of airplanes,” J. Aeronaut. Sci. 3(4), 122–128 (1936).CrossRefGoogle Scholar
Niebel, B. W., Motion and time study: An introduction to methods, time study, and wage payment. R. D. Irwin (Edition 4), 1995.Google Scholar
Mamun, A., Khaled, A., Ali, S. and Chowdhury, M., “A heuristic approach for balancing mixed-model assembly line of type I using genetic algorithm,” Int. J. Prod. Res. 50(18), 5106–5116 (2012).CrossRefGoogle Scholar
Rabbani, M., Montazeri, M., Farrokhi-Asl, H. and Rafiei, H., “A multi-objective genetic algorithm for a mixed-model assembly U-line balancing type-I problem considering human-related issues, training, and learning,” J. Ind. Eng. Int. 12(4), 485–497 (2016).CrossRefGoogle Scholar
Çil, Z. A., Mete, S. and Ağpak, K., “Analysis of the type II robotic mixed-model assembly line balancing problem,” Eng. Optim. 49(6), 990–1009 (2017).CrossRefGoogle Scholar
Ramezanian, R. and Ezzatpanah, A., “Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem,” Comput. Ind. Eng. 87, 74–80 (2015).CrossRefGoogle Scholar
Jie, Z.-m., Hou, K.-h., Zheng, Y.-j., Wang, L., Chen, C. and Zhang, Y.-f., “An Advanced Multi-Objective Genetic Algorithm for Mixed-Model Two-Sided Assembly Line Balancing,” Proceedings of the 21st International Conference on Industrial Engineering and Engineering Management 2014 (Springer, 2015) pp. 287–291.CrossRefGoogle Scholar
Zahiri, B., Tavakkoli-Moghaddam, R. and Rezaei-Malek, M., “An MCDA-DEA approach for mixed-model assembly line balancing problem under uncertainty,” J. Intell. Fuzzy Syst. 30(5), 2737–2748 (2016).CrossRefGoogle Scholar
Delice, Y., Aydoğan, E. K., Özcan, U. and İlkay, M. S., “A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing,” J. Intell. Manuf. 28(1), 23–36 (2017).CrossRefGoogle Scholar
Li, Z., Tang, Q. and Zhang, L., “Two-Sided assembly line balancing problem of type I: Improvements, a simple algorithm and a comprehensive study,” Comput. Operat. Res. 79, 78–93 (2017).CrossRefGoogle Scholar
Zhang, D. Z. and Kucukkoc, I., “Balancing mixed-model parallel two-sided assembly lines,” Proceedings of 2013 International Conference on Industrial Engineering and Systems Management (IESM) (IEEE, 2013) pp. 1–11.Google Scholar
Li, Z., Nilakantan Janardhananc, M., Tang, Q. and Ponnambalam, S. G., “Model and metaheuristics for robotic two-sided assembly line balancing problems with setup times”, Swarm Evol. Comput. 50 (2019).CrossRefGoogle Scholar
Boysen, N., Fliedner, M. and Scholl, A., “A classification of assembly line balancing problems,” Eur. J. Oper. Res. 183(2), 674–693 (2007).CrossRefGoogle Scholar
Kriengkorakot, N. and Pianthong, N., “The assembly line balancing problem: Review articles,” Eng. Appl. Sci. Res. 34(2), 133–140 (2007).Google Scholar
Sivasankaran, P. and Shahabudeen, P., “Literature review of assembly line balancing problems,” Int. J. Adv. Manuf. Tech. 73(9–12), 1665–1694 (2014).CrossRefGoogle Scholar
Girwal, A. P. P. and Porwal, M. T. S. N., “A Review Article of Balancing Assembly Line Using Particle Swarm Optimization Algorithm” (2018).Google Scholar
Scholl, A., Klein, R. and Domschke, W., “Pattern based vocabulary building for effectively sequencing mixed-model assembly lines,” J. Heurist. 4(4), 359–381 (1998).CrossRefGoogle Scholar
Xiaobo, Z. and Ohno, K., “Properties of a sequencing problem for a mixed model assembly line with conveyor stoppages,” Eur. J. Oper. Res. 124(3), 560–570 (2000).CrossRefGoogle Scholar
Rauf, M., Guan, Z., Sarfraz, S., Mumtaz, J., Shehab, E., Jahanzaib, M. and Hanif, M., “A smart algorithm for multi-criteria optimization of model sequencing problem in assembly lines”, Robot. Comput. Integr. Manuf. 61 (2020).CrossRefGoogle Scholar
Lee, W.-C., “Single-Machine scheduling with past-sequence-dependent setup times and general effects of deterioration and learning,” Opt. Lett., (8), 1–10 (2014).Google Scholar
Salehi, M., Saber, M. and Rezaeian, J., “Two meta-heuristic algorithms for parallel machines scheduling problem with past-sequence-dependent setup times and effects of deterioration and learning,” Int. J. Ind. Eng. Prod. Res. 27(1), 69–88 (2016).Google Scholar
Ruiz-Torres, A. J., Paletta, G. and M’Hallah, R., “Makespan minimisation with sequence-dependent machine deterioration and maintenance events,” Int. J. Prod. Res. 55(2), 462–479 (2017).CrossRefGoogle Scholar
Zhan, Y. and Zhang, R., “A Genetic Algorithm for the Balancing and Sequencing Problem in the Mixed-Model U-Line,” 2013 IEEE Workshop on Computational Intelligence In Production And Logistics Systems (CIPLS) (IEEE, 2013) pp. 123–130.CrossRefGoogle Scholar
Kucukkoc, I. and Zhang, D. Z., “Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines,” Int. J. Prod. Res. 52(12), 3665–3687 (2014).CrossRefGoogle Scholar
Nie, L., Bai, Y. W., Wu, J. and Pang, C. T., “Integrated approach for flexible mixed model assembly lines balancing and model sequencing problem,” Appl. Mech. Mater. 670, 1593 (2014).CrossRefGoogle Scholar
Faccio, M., Gamberi, M. and Bortolini, M., “Hierarchical approach for paced mixed-model assembly line balancing and sequencing with jolly operators,” Int. J. Prod. Res. 54(3), 761–777 (2016).CrossRefGoogle Scholar
Manavizadeh, N., Rabbani, M. and Radmehr, F., “A new multi-objective approach in order to balancing and sequencing U-shaped mixed model assembly line problem: A proposed heuristic algorithm,” Int. J. Adv. Manuf. Tech. 79, 415–425 (2015).CrossRefGoogle Scholar
Defersha, F. and Mohebalizadeh-Gashti, F., “Simultaneous balancing, sequencing, and workstation planning for a mixed model manual assembly line using hybrid genetic algorithm”, Comput. Ind. Eng. 119, 370–387 (2018).CrossRefGoogle Scholar
Zhang, B., Xu, B., L. and Zhang, J., “Balancing and sequencing problem of mixed-model U-shaped robotic assembly line: Mathematical model and dragonfly algorithm based approach”, Appl. Soft Comput. (in press).Google Scholar
Toksarı, M. D., İsleyen, S. K., Güner, E. and Baykoç, Ö. F., “Assembly line balancing problem with deterioration tasks and learning effect,” Exp. Syst. Appl. 37(2), 1223–1228 (2010).CrossRefGoogle Scholar
Holland, J. H., Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intellegence (MIT Press, Cambridge, 1992).CrossRefGoogle Scholar
Niaki, S. T. A. and Ershadi, M. J., “A parameter-tuned genetic algorithm for statistically constrained economic design of multivariate CUSUM control charts: ATaguchi loss approach,” Int. J. Syst. Sci. 43(12), 2275–2287 (2012).CrossRefGoogle Scholar