Model to determine Optimization number of work stations and balancing line by using genetic algorithm in the saipa company

Subject Areas : Industrial Management

saeed amini ¹ , Reza Ehtesham Rasi ² , vahid amini ³ , hosein taheri ⁴

1 - MSc ,Department of Business Management , Department of Management and Accounting , Islamic Azad University Qazvin Branch , Qazvin, Iran
2 - Assistant Prof , Department of Management and Accounting , Islamic Azad University Qazvin Branch , Qazvin, Iran
3 - MSc , Department of mining Engineering , Department of mining faculty , Tarbiat Modares University ، Tehran ،iran
4 - Ph.D. Student , Sport Management, Department of Management and Accounting , Shahrood University of Technology, Semnan, Iran

Received: 2018-09-27 Accepted : 2019-04-28 Published : 2019-05-25

Keywords: Multi-Criteria Decision Making, Genetic algorithm(Meta Innovative), Optimization, Keywords: Assembly line balancing,

Abstract :

Given the importance and attractiveness of the mixed product parallel lines balancing problem and the importance of the human factor in the development process and also very few studies in relation to several simultaneous objectives that is a new approach in the field of parallel assembly lines, this paper presents a two-objective combined integer mathematical model for balancing mixed-model parallel assembly lines. Because of the complexity of the problem is in the class of NP-hard problems, the mathematical model can’t be solved for the actual dimensions of the industry. Genetic algorithm was used to solve this problem, coding and designing is done with MATLAB. The proposed model is evaluated by data set provided in this area. Results reflect the good performance of the model and improved objectives. Therefore proposed model leads to reduce the number of stations required in lines and minimize the total cost of human resources in mixed model parallel assembly lines.

References:

1. Aladwani, A.M. (2001). Online banking: A field study of drivers, development challenges, and expectations. [Electronic version]. International Journal of Information Management, 21(3), 213-225.


2. Alam-Tabriz, A., & Mohmmadrahimi A. (2013). Meta-heuristic algorithms in combinatorial optimization. Tehran: Safar Publication (In Persian).


3. Amin zinal zade, A.(2010).Using a mathematical model for the balancing of assembly lines. Journal of Beyond Management, 3 (1), 7-30. (In Persian)


4. Arcus, A. (1965). A computer method of sequencing operations for assembly lines. International Journal of Production Research, 4(4), 259-277.


5. Askin, R., & Zhou, M. (1997). A parallel station heuristic for the mixed-model production line balancing problem. International Journal of Production Research, 35(11), 3095-3106.


6. Bard, J. (1989). Assembly line balancing with parallel workstations and dead time. The International Journal of Production Research, 27(6), 1005-1018.


7. Assaf, G., Josiassen, A., Gillen, D., (2013),Measuring firm performance: Bayesian estimates with good and bad outputs,Journal of Business Research, 2013.


8. Bautista, J., & Pereira, J. (2007). Ant algorithms for a time and space constrained assembly line balancing problem. European Journal of Operational Research, 177(3), 2016-2032.


9. Baybars, I. (1986). A survey of exact algorithms for the simple assembly line balancing problem. Management science, 32(8), 909-932.


10. Baybars, I. (1986). A survey of exact algorithms for the simple assembly line balancing problem. Management science, 32(8), 909-932.


11. Becker, C., & Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European journal of operational research, 168(3), 694-715.


12. Becker, C., & Scholl, A. (2009). Balancing assembly lines with variable parallel workplaces: Problem definition and effective solution procedure. European journal of operational research, 199(2), 359-374.


13. Boysen, N., Fliedner, M., & Scholl, A. (2008). Assembly line balancing: which model to use when? International Journal of Production Economics, 111(2), 509-528.


14. Bukchin, Y., & Rabinowitch, I. (2006). A branch-and-bound based solution approach for the mixed-model assembly line-balancing problem for minimizing stations and task duplication costs. European Journal of Operational Research, 174(1), 492-508.


15. Chan, A. T., Ngai, E. W., & Moon, K. K. (2017). The effects of strategic and manufacturing flexibilities and supply chain agility on firm performance in the fashion industry. European Journal of Operational Research, 259(2), 486-499.


16. Chutima, P., & Naruemitwong, W. (2014). A Pareto biogeography-based optimisation for multi-objective two-sided assembly line sequencing problems with a learning effect. Computers & Industrial Engineering, 69, 89-104.


17. Esmaeilian, G., Sulaiman, S., Ismail, N., Ahmad, M., & Hamedi, M. (2008). Application of MATLAB to Create Initial Solution for Tabu Search in Parallel Assembly Lines Balancing. International Journal of Computer Science and Network Security, 8, 132-136.


18. Erel, E., & Sarin, S. C. (1998). A survey of the assembly line balancing procedures. Production Planning & Control, 9(5), 414-434.


19. Gokcen, H., & Erel, E. (1998). Binary integer formulation for mixed-model assembly line balancing problem. Computers & Industrial Engineering, 34(2), 451-461.


20. Gokcen, H., Agpak, K., & Benzer, R. (2006). Balancing of parallel assembly lines. International Journal of Production Economics, 103(2), 600-609.


21. Gisbert,A., Navallas, B. (2015).The association between voluntary disclosure and corporate governance in the presence of severe agency conflicts,Advances in Accounting, incorporating Advances in International Accounting, 22(28), 171,350.


22. Hazbany, S., Gilad, I., & Shpitalni, M. (2007). About the efficiency and cost reduction of parallel mixed-model assembly lines The Future of Product Development (pp. 483-492): Springer.


23. Jiapeng, L., Xiuwu, L., Zhao, W., & Yang, N. (2015). A classification approach based on the outranking model for multiple criteria ABC analysis.


24. Kima,D. H., Kima, J., Byunb,Y., Chunc, S.H. (2014). A Study on the   Effect   of   Governance   Adequacy   on   the   Corporate Performance, Procedia -Social and Behavioral Sciences 107, 2013,59–


25. Lusa, A. (2008). A survey of the literature on the multiple or parallel assembly line balancing problem. European Journal of Industrial Engineering, 2(1), 50-72.


26. Lim, M., How, J., Verhoeven, P. (2013). Corporate ownership, corporate governance reform and 4 timeliness of earnings: Malaysian evidence,Journal of Contemporary Accounting & economics,1(5), 1–14.


27. Martinez-Sanchez, A., & Lahoz- Leo, F. (2018). Supply chain agility: a mediator for absorptive capacity. Baltic Journal of Management, 13(10), 20-28.


28. Mc., M.S & S.M., Gupta. (2005). A balancing method and genetic algorithm for disassembly line balancing",European Journal of Operational Research, 40(179).


29. Mohammad taghi taghavi fard , M.( 2012 ) . A new mathematical model of multi-product assembly line balancing problem. Journal of Industrial Management, 3(6), 1-16. (in Persian).


30. Mohammadi, G. (2015). Multi-objective flow shop production scheduling via robust genetic algorithms optimization technique.International Journal of Service Science, Management and Engineering, 2(1), 1-8 (In Persian).


31. Miltenburg, J. (2002). Balancing and scheduling mixed-model U-shaped production lines. International Journal of Flexible Manufacturing Systems, 14(2), 119-151.


32. McMullen, P. R., & Frazier, G. V. (1997). A heuristic for solving mixed-model line balancing problems with stochastic task durations and parallel stations. International Journal of Production Economics, 51(3), 177-190.


33. Momeni, Mansour & Qayyomi, Ali. (2010). Statistical analysis using spss, Tehran, Ganj Shayegan Publication. (In Persian).


34. Negahban, A. (2005). Guidance for research method using spss11 questionnaire. Publisher: Jahad Daneshgahi. (In Persian)


35. Ozcan, U., & Toklu, B. (2009). A new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems. Journal of Intelligent Manufacturing, 20(1), 123-136.


36. Raff, D., & Summers, L. H. (1989). Did Henry Ford pay efficiency wages? : National Bureau of Economic Research Cambridge, Mass., USA.


37. Rekiek, B., & Delchambre, A. (2006). Assembly line design: the balancing of mixed-model hybrid assembly lines with genetic algorithms: Springer.


38. Seyed Esfehani, M.M., Heidari, M. & Jaberi, S.(2014).Tabrid simulation algoritm representation for optimization of parallel serial system certainty,k-from-n substitution by fuzzy parameters .International Journal of Industrial Engineering and Production Management, 4(24), 414-422.(In Persian).


39. Sparling, D., & Miltenburg, J. (1998). The mixed-model U-line balancing problem. International Journal of Production Research, 36(2), 485-501.


40. Toksari, M. D., lsleyen, S. K., Guner, E., & Faruk, B. m. (2012). Assembly line balancing problem with deterioration tasks and learning effect. Expert Systems with Applications, 37(2), 1223-1228.


41. Taghizadeh, Hooshang, Zinolzadeh, Amin (2009). The application of weight-priority hypersurfic methods and the longest operation time (LCR) of aligning assembly lines and their impact on the organization's performance. Journal of Industrial Engineering and Production Management, 3(20), 44-55.


42. Taghavi Fard, M.(2013). A new mathematical model for solving the balancing problem of multi product assembly lines Industrial Management Journal, 3 (6), 1-16.(In Persian)


43. Vilarinho, P. M., & Simaria, A. S. (2002). A two-stage heuristic method for balancing mixed-model assembly lines with parallel workstations. International Journal of Production Research, 40(6), 1405-1420.