A novel robust possibilistic cellular manufacturing model considering worker skill and product quality

Document Type : Article

Authors

Department of Industrial and Systems Engineering, Mazandaran University of Science and Technology, Babol, Iran

Abstract

Design of an appropriate cellular manufacturing system (CMS) leads to system flexibility and production efficiency by using the similarities in the manufacturing process of products. One of the main issues in these systems is to consider product quality level and worker’s skill level in the production process. This study proposes a comprehensive bi-objective possibilistic nonlinear mixed-integer programming model under uncertain environment to design a suitable CMS with aims of minimizing the total costs and total inaction of workers and machines, simultaneously. In this respect, the demand of each product with a specific quality level and linguistic parameters such as product quality level, worker’s skill level and job hardness level on machines are considered under fuzzy environment. To this end, the robust possibilistic programming approach is tailored to cope with fuzzy impute parameters. Finally, a real case study is provided to show the efficiency and applicability of the proposed model. In this respect, the proposed approach could be improved the total costs by 23.6% and the total inaction of workers and machines by 11.7% regarding the real practice. In addition, the performance of the presented model is demonstrated by comparing between the results obtained from the proposed model and actual practice.

Keywords

Main Subjects


Refrences:
1.Wemmerlov, U. and Hyer, N.L. Procedures for the part family/machine group identi_cation problem in cellular manufacturing", Journal of Operations Management, 6(2), pp. 125-147 (1986).
2. Aalaei, A. and Davoudpour, H. A robust optimization model for cellular manufacturing system into supply chain management", International Journal of Production Economics, 183, pp. 667-679 (2017).
3. Rafiei, H. and Ghodsi, R. A bi-objective mathematical model toward dynamic cell formation considering labor utilization", Applied Mathematical Modelling, 37(4), pp. 2308-2316 (2013). 4. Rheault, M., Drolet, J.R., and Abdulnour, G. Physically recon_gurable virtual cells: a dynamic model for a highly dynamic environment", Computers & Industrial Engineering, 29(1), pp. 221-225 (1995). 5. Rosenblatt, M.J. The dynamics of plant layout", Management Science, 32(1), pp. 76-86 (1986). 6. Mahdavi, I., Aalaei, A., Paydar, M.M., and Solimanpur, M. Designing a mathematical model for dynamic cellular manufacturing systems considering production planning and worker assignment", Computers & 554 A. Hashemoghli et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 538{556 Mathematics with Applications, 60(4), pp. 1014-1025 (2010). 7. Saxena, L.K. and Jain, P.K. Dynamic cellular manufacturing systems design-a comprehensive model", The International Journal of Advanced Manufacturing Technology, 53(1-4), pp. 11-34 (2011). 8. Paydar, M.M., Saidi-Mehrabad, M., and Kia, R. Designing a new integrated model for dynamic cellular manufacturing systems with production planning and intra-cell layout", International Journal of Applied Decision Sciences, 6(2), pp. 117-143 (2013). 9. Kia, R., Javadian, N., and Tavakkoli-Moghaddam, R. A simulated annealing algorithm to determine a group layout and production plan in a dynamic cellular manufacturing system", Journal of Optimization in Industrial Engineering, 7(14), pp. 37-52 (2014). 10. Norman, B.A., Tharmmaphornphilas, W., Needy, K.L., Bidanda, B., and Warner, R.C. Worker assignment in cellular manufacturing considering technical and human skills", International Journal of Production Research, 40(6), pp. 1479-1492 (2002). 11. Suksawat, B., Hiraoka, H., and Ihara, T. A new approach manufacturing cell scheduling based on skill-based manufacturing integrated to genetic algorithm", In Towards Synthesis of Micro-/Nano- Systems, Springer, pp. 325-326 (2007). 12. Duan, F., Tan, J.T.C., Tong, J.G., Kato, R., and Arai, T. Application of the assembly skill transfer system in an actual cellular manufacturing system", Automation Science and Engineering, IEEE Transactions on, 9(1), pp. 31-41 (2012). 13. Egilmez, G., Erenay, B., and Suer, G.A. Stochastic skill-based manpower allocation in a cellular manufacturing system", Journal of Manufacturing Systems, 33(4), pp. 578-588 (2014). 14. Lim, Z.Y., Ponnambalam, S., and Izui, K. Multiobjective hybrid algorithms for layout optimization in multi-robot cellular manufacturing systems", Knowledge-Based Systems, 120, pp. 87-98 (2017). 15. Rezazadeh, H. and Khiali-Miab, A. A two-layer genetic algorithm for the design of reliable cellular manufacturing systems", International Journal of Industrial Engineering Computations, 8(3), pp. 315-332 (2017). 16. Aalaei, A. and Davoudpour, H. Revised multi-choice goal programming for incorporated dynamic virtual cellular manufacturing into supply chain management: a case study", Engineering Applications of Arti_cial Intelligence, 47, pp. 3-15 (2016). 17. Forghani, K. and Mohammadi, M. A genetic algorithm for solving integrated cell formation and layout problem considering alternative routings and machine capacities", Scientia Iranica. Transaction E, Industrial Engineering, 21(6), pp. 2326-2346 (2014). 18. Sahinidis, N.V. Optimization under uncertainty: state-of-the-art and opportunities", Computers & Chemical Engineering, 28(6), pp. 971-983 (2004). 19. Mirzapour Al-E-Hashem, S., Malekly, H., and Aryanezhad, M. A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty", International Journal of Production Economics, 134(1), pp. 28-42 (2011). 20. Safaei, N. and Tavakkoli-Moghaddam, R. An extended fuzzy parametric programming-based approach for designing cellular manufacturing systems under uncertainty and dynamic conditions", International Journal of Computer Integrated Manufacturing, 22(6), pp. 538-548 (2009). 21. Kia, R., Paydar, M.M., Jondabeh, M.A., Javadian, N., and Nejatbakhsh, Y. A fuzzy linear programming approach to layout design of dynamic cellular manufacturing systems with route selection and cell recon _guration", International Journal of Management Science and Engineering Management, 6(3), pp. 219- 230 (2011). 22. Behret, H. and Satoglu, S.I. Fuzzy logic applications in cellular manufacturing system design", In Computational Intelligence Systems in Industrial Engineering, Springer, pp. 505-533 (2012). 23. Paydar, M.M. and Saidi-Mehrabad, M. Revised multi-choice goal programming for integrated supply chain design and dynamic virtual cell formation with fuzzy parameters", International Journal of Computer Integrated Manufacturing, 28(3), pp. 251-265 (2014). 24. Tavakkoli-Moghaddam, R., Javadian, N., Javadi, B., and Safaei, N. Design of a facility layout problem in cellular manufacturing systems with stochastic demands", Applied Mathematics and Computation, 184(2), pp. 721-728 (2007). 25. Ghezavati, V. and Saidi-Mehrabad, M. Designing integrated cellular manufacturing systems with scheduling considering stochastic processing time", The International Journal of Advanced Manufacturing Technology, 48(5-8), pp. 701-717 (2010). 26. Ghezavati, V. and Saidi-Mehrabad, M. An e_cient hybrid self-learning method for stochastic cellular manufacturing problem: A queuing-based analysis", Expert Systems with Applications, 38(3), pp. 1326- 1335 (2011). 27. Egilmez, G., Suer, G.A., and Huang, J. Stochastic cellular manufacturing system design subject to maximum acceptable risk level", Computers & Industrial Engineering, 63(4), pp. 842-854 (2012). 28. Salarian, R., Fazlollahtabar, H., and Mahdavi, I. Inter-cell movement minimisation in a cellular manufacturing system having stochastic parameters", International Journal of Services and Operations Management, 17(1), pp. 67-87 (2014). A. Hashemoghli et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 538{556 555 29. Bagheri, M., Sadeghi, S., and Saidi-Mehrabad, M. A benders decomposition approach for dynamic cellular manufacturing system in the presence of unreliable machines", Journal of Optimization in Industrial Engineering, 8(17), pp. 37-49 (2015). 30. Rabbani, M., Akbari, E., and Dolatkhah, M. Manpower allocation in a cellular manufacturing system considering the impact of learning, training and combination of learning and training in operator skills", Management Science Letters, 7(1), pp. 9-22 (2017). 31. Ghezavati, V., Sadjadi, S., and Dehghan Nayeri, M. Integrating strategic and tactical decisions to robust designing of cellular manufacturing under uncertainty: Fixed suppliers in supply chain", International Journal of Computational Intelligence Systems, 4(5), pp. 837- 854 (2011). 32. Forghani, K., Mohammadi, M., and Ghezavati, V. Designing robust layout in cellular manufacturing systems with uncertain demands", International Journal of Industrial Engineering Computations, 4(2), pp. 215- 226 (2013). 33. Tavakkoli-Moghaddam, R., Sakhaii, M., and Vatani, B. A robust model for a dynamic cellular manufacturing system with production planning", International Journal of Engineering-Transactions A: Basics, 27(4), pp. 587-598 (2013). 34. Sakhaii, M., Tavakkoli-Moghaddam, R., Bagheri, M., and Vatani, B. A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines", Applied Mathematical Modelling, 40(1), pp. 169-191 (2016). 35. Paydar, M.M. and Saidi-Mehrabad, M. Revised multi-choice goal programming for integrated supply chain design and dynamic virtual cell formation with fuzzy parameters", International Journal of Computer Integrated Manufacturing, 28(3), pp. 251-265 (2015). 36. Pishvaee, M., Razmi, J., and Torabi, S. Robust possibilistic programming for socially responsible supply chain network design: A new approach", Fuzzy Sets and Systems, 206, pp. 1-20 (2012). 37. Zhang, W. and Reimann, M. A simple augmented _-constraint method for multi-objective mathematical integer programming problems", European Journal of Operational Research, 234(1), pp. 15-24 (2014). 38. Defersha, F.M. and Chen, M. A comprehensive mathematical model for the design of cellular manufacturing systems", International Journal of Production Economics, 103(2), pp. 767-783 (2006). 39. Mahdavi, I., Aalaei, A., Paydar, M.M., and Solimanpur, M. Production planning and cell formation in dynamic virtual cellular manufacturing systems with worker exibility", In Computers & Industrial Engineering, 2009. CIE 2009, International Conference on. 2009: IEEE (2009). 40. Solimanpur, M., Saeedi, S., and Mahdavi, I. Solving cell formation problem in cellular manufacturing using ant-colony-based optimization", The International Journal of Advanced Manufacturing Technology, 50(9- 12), pp. 1135-1144 (2010). 41. Mahdavi, I., Aalaei, A., Paydar, M.M., and Solimanpur, M. Multi-objective cell formation and production planning in dynamic virtual cellular manufacturing systems", International Journal of Production Research, 49(21), pp. 6517-6537 (2011). 42. Kia, R., Baboli, A., Javadian, N., Tavakkoli- Moghaddam, R., Kazemi, M., and Khorrami, J. Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and exible recon_guration by simulated annealing", Computers & Operations Research, 39(11), pp. 2642-2658 (2012). 43. Torabi, S. and Amiri, A.S. A possibilistic approach for designing hybrid cellular manufacturing systems", International Journal of Production Research, 50(15), pp. 4090-4104 (2012). 44. Chang, C.-C., Wu, T.-H., and Wu, C.-W. An e_cient approach to determine cell formation, cell layout and intracellular machine sequence in cellular manufacturing systems", Computers & Industrial Engineering, 66(2), pp. 438-450 (2013). 45. Kia, R., Shirazi, H., Javadian, N., and Tavakkoli- Moghaddam, R. A multi-objective model for designing a group layout of a dynamic cellular manufacturing system", Journal of Industrial Engineering International, 9(1), pp. 1-14 (2013). 46. Shirazi, H., Kia, R., Javadian, N., and Tavakkoli- Moghaddam, R. An archived multi-objective simulated annealing for a dynamic cellular manufacturing system", Journal of Industrial Engineering International, 10(2), pp. 1-17 (2014). 47. Deep, K. and Singh, P.K. Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm", Journal of Manufacturing Systems, 35, pp. 155-163 (2015).