A genetic algorithm approach for a dynamic cell formation problem considering machine breakdown and buffer storage

Document Type : Research Paper

Authors

1 School of Industrial Engineering, College of Engineering, University of Tehran

2 School of Industrial and System Engineering, College of Engineering, University of Tehran

Abstract

Cell formation problem mainly address how machines should be grouped and parts be processed in cells. In dynamic environments, product mix and demand change in each period of the planning horizon. Incorporating such assumption in the model increases flexibility of the system to meet customer’s requirements. In this model, to ensure the reliability of the system in presence of unreliable machines, alternative routing process as well as buffer storage is considered to reduce detrimental effects of machine failure. This problem is presented by a nonlinear mixed integer programming model attempting to minimize the overall cost of the system. To solve the model in large scale for practical purposes, a genetic algorithm approach is adopted as the model belongs to NP-hard class of problem. A numerical example is used both, for small-sized and large-sized instances to show the validity and efficiency of the method in finding near optimal solution.

Keywords

References

1. Ahkioon, S., Bulgak, A.A. & Bektas, T. (2009a). Cellular manufacturing systems design with routing flexibility, machine procurement, production planning and dynamic system reconfiguration. International Journal of Production Research, 47, 1573–1600.
2. Ahkioon, S., Bulgak, A.A. & Bektas, T. (2009b). Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration. European Journal of Operational Research, 192, 414 – 428.
3. Aramoon Bajestani, M. Rabbani, M. Rahimi-Vahed, A.R. & Baharian, G.K. (2009). A multi-objective scatter search for a dynamic cell formation problem. Computers & Operations Research, 36, 777 – 794.
4. Arkat, J., Naseri, F. & Ahmadizar, F. (2011). A stochastic model for the generalized cell formation problem considering machine reliability. International Journal of Computer Integrated Manufacturing, 24, 1095 – 1102.
5. Balakrishnan, J. & Cheng, C. H. (2007). Multi-period planning and uncertainty issues in cellular manufacturing: a review and future directions. European Journal of Operational Research, 177, 281-309.
6. Chang, C. C., Wu, T. H. & Wu, C. W. (2013). An efficient approach to determine cell formation, cell Layout and intracellular machine sequence in cellular manufacturing systems. Computers & Industrial Engineering, 66, 438 – 450.
7. Carlos, A. & Sebastia´n, L. (2006). A particle swarm optimization algorithm for part–machine grouping.
Robotics and Computer-Integrated Manufacturing, 22, 468 – 474.
8. Chung, S.H. & Chang, C.C. (2010). An efficient tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations. Computers & Industrial Engineering, 60, 7-15.
9. Das, K., Lashkari, R. S. & Sengupta, S. (2007a). Reliability consideration in the design and analysis of cellular manufacturing systems. International Journal of Production Economics, 105, 243-262
10. Das, K., Lashkari, R. S. & Sengupta, S. (2007b). Machine reliability and preventive maintenance planning for cellular manufacturing systems. European Journal of Operational Research, 183, 162-180.
11. Defersha, F. H. & Chen, M. (2008). A linear programming embedded genetic algorithm for an integrated cell formation and lot sizing considering product quality. European Journal of Operational Research, 187, 46-69.
12. Diallo, M., Pierreval, H. & Quilliot, A. (2001). Manufacturing cells design with flexible routing capability in presence of unreliable machines. International Journal of production economics, 74, 175 – 182.
13. Ghezavati, V. R., & Saidi-Mehrabad, M. (2011). An efficient hybrid self-learning method for stochastic cellular manufacturing problem: A queuing-based analysis. Expert Systems with Applications, 38, 1326 – 1335.
14. Ghosh, T., Sengupta, S., Chattopadhyay, M., & Dan, P. (2011). Meta-heuristics in cellular manufacturing: A state-of-the-art review. International Journal of Industrial Engineering Computations, 2, 87-122.
15. JabalAmeli, M.S., Arkat, J. & Barzinpour, F. (2008). Modelling the effects of machine breakdowns in the generalized cell formation problem. International Journal of Advanced Manufacturing Technology, 39, 838 – 850.
16. JabalAmeli, M.S. & Arkat, J. (2008). Cell formation with alternative process routings and machine reliability consideration. International Journal of Advanced Manufacturing Technology, 35, 761 – 768.
17. Kusiak, A. & Chow, W.S. (1988). Decomposition of manufacturing systems. IEEE Journal of Robotics and Automation, 4, 457 – 471.
18. Lee, S.D. (2000). Buffer sizing in complex cellular manufacturing systems. International Journal of Systems Science, 31, 937 – 948.
19. Mahdavi, I., Paydar, M. M., Solimanpur, M., & Heidarzade, A. (2009). Genetic algorithm approach for solving a cell formation problem in cellular manufacturing. Expert Systems with Applications, 36, 6598 – 6604.
20. Mansouri, S.A., Husseini, S.M.M. & Newman, S.T. (2000). A review of modern approaches to multi-criteria cell design.
International Journal of Production Research, 38, 1201 – 1218.
21. Nsakanda, A. L., Diaby, M. & Price, W. L. (2006). Hybrid genetic approach for solving large-scale capacitated cell formation problems with multiple routings. European Journal of Operational Research, 171, 1051 – 1070.
22. Nouri, H., & Hong, T. S. (2013). Development of bacteria foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations. Journal of Manufacturing Systems, 32, 20 – 31.
23. Papaioannou, G. & Wilson, J. M. (2010). The evolution of cell formation problem methodologies based on recent studies (1997–2008): Review and directions for future research. European Journal of Operational Research, 206, 509 – 521.
24. Rafiei, H., & Ghodsi, R. (2013). A bi-objective mathematical model toward dynamic cell formation considering labor utilization. Applied Mathematical Modelling, 37, 2308 – 2316.
25. Renna, P., & Ambrico, M. (2015). Design and reconfiguration models for dynamic cellular manufacturing to handle market changes. International Journal of Computer Integrated Manufacturing, 28, 170 – 186.
26. Safaei, N., Saidi-Mehrabad, M., Tavakkoli-Moghaddam, R. & Sassani, F. (2008a). A fuzzy programming approach for a cell formation problem with dynamic and uncertain conditions. Fuzzy Sets and Systems, 159, 215 – 236.
27. Safaei, N., Saidi-Mehrabad, M. & JabalAmeli, M. S. (2008b). A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system. European Journal of Operational Research, 185, 563 – 592.
28. Saidi-Mehrabad, M. & Safaei, N. (2007). A new model of dynamic cell formation by a neural approach. International Journal of Advanced Manufacturing Technology, 33, 1001 – 1009.
29. Sakhaii, M., Tavakkoli-Moghaddam, R., Bagheri, M., & Vatani, B. (2015). A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines. Applied Mathematical Modelling, doi:10.1016/j.apm.2015.05.005.
30. Saxena, L.K. & Jain, P.K. (2011). Dynamic cellular manufacturing systems design—a comprehensive model. International Journal of Advanced Manufacturing Technology, 53, 11 – 34.
31. Uddin, M. K., & Shanker, K. (2002). Grouping of parts and machines in presence of alternative process routes by genetic algorithm. International Journal of Production Economics, 76, 219 – 228.
32. Venugopal, V., & Narendran, T. T. (1992). A genetic algorithm approach to the machine-component grouping problem with multiple objectives. Computers & Industrial Engineering, 22, 469 – 480.
33. Wemmerlov, U. & Hyer, N.L. (1986). Procedures for the part family machine group identification problem in cellular manufacturing. Journal of Operations Management, 6, 125 – 147.
34. Won, Y. & Currie, K.R. (2006). An Effective P-median Model Considering Production Factors in Machine Cell/Part Family Formation. Journal of Manufacturing Systems, 25, 58 – 64.

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