Designing a new bi-objective mathematical model for dynamic cell configuration based on grouping efficacy by considering operator assignments

Document Type : Research Paper

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Abstract

In the present competitive world, the necessity of minimizing costs and production time and increasing the productivity in manufacturing systems are more and more felt. Because when production costs are reduced, the final price of product is reduced too and when the production time is reduced, afterward the response time to customers order is reduced too. This paper presents a bi-objective mathematical model of multi period cell formation problem base on grouping efficacy in dynamic environment with the flexibility in operator assignment. The advantages of the proposed model are as follows: considering multi period planning horizon, dynamic system reconfiguration, duplicate machine, machine capacity, available time of operators and operator assignment. The aims of the proposed model are to maximize the total value of grouping efficacy (TVGE) and minimize the total costs (TC) include purchasing new machines cost, machine overhead cost, machine processing and reconfiguration costs, hiring, firing and salary costs. Computational results are presented by solving some numerical examples with improved e-constraint method to validate and verify the proposed model.

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Mitranov, S. P. (1966). The Scientific Principles of Group Technology. National Lending Library For Science and Technology (Great Britain)
2. Burbidge, J. L. (1971). “Production Flow Analysis”, Production Engineer, Vol. 4, No. 50, PP. 139-152.
3. Heragu, S. S. (1994). “Group Technology and Cellular Manufacturing”, Systems, Man and Cybernetics, IEEE Transactions on, Vol. 24, No. 2, PP. 203-215.
4. Wemmerlöv, U., and Hyer, N. L. (1989). “Cellular Manufacturing in the US Industry: A Survey of Users”, The International Journal of Production Research, Vol. 27, No. 9, PP. 1511-1530.
5. Mahdavi, I. et al. (2007). “Designing a New Mathematical Model for Cellular Manufacturing System Based on Cell Utilization”, Applied Mathematics and Computation, Vol. 190, No. 1, PP. 662-670.
6. Mahdavi, I., and Mahadevan, B. (2008). “CLASS: An Algorithm for Cellular Manufacturing System and Layout Design Using Sequence Data”, Robotics and Computer-Integrated Manufacturing, Vol. 24, No. 3, PP. 488-497.
7. Tavakkoli-Moghaddam, R., Safaei, N., and Sassani, F. (2008). “A New Solution for a Dynamic Cell Formation Problem wth Alternative Routing and Machine Costs Using Simulated Annealing”, Journal of the Operational Research Society, Vol. 59, No. 4, PP. 443-454.
8. Mahdavi, I. et al. (2009). “Genetic Algorithm Approach for Solving a Cell Formation Problem in Cellular Manufacturing”, Expert Systems with Applications, Vol. 36, No. 3, PP. 6598-6604.
9. Paydar, M. M., and Saidi-Mehrabad, M. (2013). “A Hybrid Genetic-Variable Neighborhood Search Algorithm for the Cell Formation Problem Based on Grouping Efficacy”, Computers and Operations Research, Vol. 40, No. 4, PP. 980-990.
10. Aryanezhad, M. B., Deljoo, V., and Mirzapour Al-E-Hashem, S. M. J. (2009). “Dynamic Cell Formation and the Worker Assignment Problem: A New Model”, The International Journal of Advanced Manufacturing Technology, Vol. 41, No. 3-4, PP. 329-342.
11. Bajestani, M. A. et al. (2009). “A Multi-Objective Scatter Search for A Dynamic Cell Formation Problem”, Computers and Operations Research, Vol. 36, No. 3, PP. 777-794.
12. Bagheri, M., and Bashiri, M. (2014). “A New Mathematical Model Towards the Integration of Cell Formation with Operator Assignment and Inter-Cell Layout Problems in a Dynamic Environment”, Applied Mathematical Modelling, Vol. 38, No. 4, PP. 1237-1254.
13. Mahdavi, I. et al. (2012). “A New Mathematical Model for Integrating All Incidence Matrices in Multi-Dimensional Cellular Manufacturing System”, Journal of Manufacturing Systems, Vol. 31, No. 2, PP. 214-223.
14. Paydar, M. M., and Saidi-Mehrabad, M. (2015). “Revised Multi-Choice Goal Programming for Integrated Supply Chain Design and Dynamic Virtual Cell Formation with Fuzzy Parameters”, International Journal of Computer Integrated Manufacturing, Vol. 28, No. 3, PP. 251-265.
15. Mahdavi, I. et al. (2010). “Designing a Mathematical Model for Dynamic Cellular Manufacturing Systems Considering Production Planning and Worker Assignment”, Computers and Mathematics with Applications, Vol. 60, No. 4, PP. 1014-1025.
16. Kia, R. et al. (2013). “A Simulated Annealing for Intra-Cell Layout Design of Dynamic Cellular Manufacturing Systems with Route Selection, Purchasing Machines and Cell Reconfiguration”, Asia-Pacific Journal of Operational Research, Vol. 30, No. 4.
17. Haimes, Y. Y., Lasdon, L. S., and Wismer, D. A. (1971). “On A Bicriterion Formulation of the Problems of Integrated System Identification and System Optimization”, IEEE Transactions on Systems Man and Cybernetics, Vol. 1, No.3, PP. 296-297.
18. Chankong, V., and Haimes, Y. Y. (1983). Multi-Objective Decision Making: Theory and Methodology. North-Holland.
19. Mavrotas, G. (2009). “Effective Implementation of the Ε-Constraint Method in Multi-Objective Mathematical Programming Problems”, Applied Mathematics and Computation, Vol. 213, No. 2, PP. 455-465.