Optimizing the Number of Cells in a Cellular Manufacturing System
Authors
Abstract
In this paper, the solution of a non-linear model of Cell Manufacturing (CM) in certain and dynamic conditions is
studied, considering intracellular and extracellular costs, cell constructing costs, the cost of restoration and the cost of equipment
transportation per distance travelled. Since the number of cells in each stage of production is important, by optimizing the
number of cells, additional costs can be minimized. Therefore, the main objective of this study is to investigate the optimal
number of cells located. Bio-geographical Based Optimization (BBO) algorithm is applied in the CM for the first time in the
literature and the obtained results from this algorithm are compared with the results of well-known genetic algorithm. The results
shows the good performance of genetic algorithm. Finally, the conclusion and future research are provided.
studied, considering intracellular and extracellular costs, cell constructing costs, the cost of restoration and the cost of equipment
transportation per distance travelled. Since the number of cells in each stage of production is important, by optimizing the
number of cells, additional costs can be minimized. Therefore, the main objective of this study is to investigate the optimal
number of cells located. Bio-geographical Based Optimization (BBO) algorithm is applied in the CM for the first time in the
literature and the obtained results from this algorithm are compared with the results of well-known genetic algorithm. The results
shows the good performance of genetic algorithm. Finally, the conclusion and future research are provided.
Keywords
2. Safaei, N., Saidi-Mehrabad, M., and Jabal-Ameli, M.
S., “A Hybrid Simulated Annealing for Solving an
Extended Model of Dynamic Cellular Manufacturing
System”, European Journal of Operational
Research, Vol. 185, pp. 563-592, 2008.
3. Balakrishnan, J., and Cheng, C. H., “Dynamic
Cellular Manufacturing under Multi Period Planning
Horizons”, Journal of Manufacturing Technology
Management, Vol. 16, No. 5, pp. 516-530, 2005.
4. Tavakkoli- Moghadam, R., Aryanezha, M. B., Safaei,
N., and Azaron, A., “Solving a Dynamic Cell
Formation Problem using Meta-Heuristics”, Applied
Mathematical and Computation, Vol. 170, pp 761-
780, 2005.
5. Defersha, F., and Chen, M., “A Comprehensive
Mathematical Model for the Design of Cellular
Manufacturing System”, International Journal of
Production Economics, Vol. 103, No. 2, pp. 767-783,
2006.
6. 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,
Vol. 184, pp. 721-728, 2007.
7. Saidi-Mehrabad, M., and Safaei, N., “A New Model
of Dynamic Cell Formation by a Neural Approach”,
International Journal Advance Manufacturing
Technology, Vol. 33, pp. 1001-1009, 2007.
8. Schaller, J., “Designing and Redesigning Cellular
Manufacturing System to Handle Demand Changes”,
Computer & Industrial Engineering, Vol. 53, pp.
478-490, 2007.
9. Defersha, F., and Chen, M., “A Parallel Genetic
Algorithm for Dynamic Cell Formation in Cellular
Manufacturing System”, International Journal of
Production Recearch, Vol. 46, No. 22, pp. 6389-
6413, 2008.
10. 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 and Mathematics with
Applications, Vol. 60, pp. 1014-1025, 2010.
11.Deljoo, V., Mirzapour Al-e-hashem S. M. J., Deljoo,
F., and Aryanezhad, M. B, “Using Genetic
Algorithm to Solve Dynamic Cell Formation
Problem”, Applied Mathematical Modeling, Vol. 34,
pp. 1078-1092, 2010.
12.Durán, O., Rodriguez, N., and Consalter, L. A.,
“Collaborative Particle Swarm Optimization with a
Data Mining Technique for Manufacturing Cell
Design”, Expert Systems with Applications Vol. 37,
pp. 1563-1567, 2010.
13. Ghotboddini, M. M., Rabbani, M., and Rahimian,
H., “A Comprehensive Dynamic Cell Formation
Design: Benders’ Decomposition Approach”, Expert
Systems with Applications, Vol. 38, pp. 2478-2488,
2011.
14.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
Flexible Reconfiguration by Simulated Annealing”,
Computers & Operations Research, Vol. 39, pp.
2642-2658, 2012.
15. Chen, M., and Dong C., “Coordinating Production
Planning in Cellular Manufacturing Environment
using Tabu Search”, Computers & Industrial
Engineering, Vol. 46, pp. 3571-588, 2004.
16. Simon, D., “Biogeography-based Optimization,
Evolutionary Computation”, IEEE Transactions,
Vol. 12, No. 6, pp. 702-713, 2008.
17.Holland, J. L., Making Vocational Choices, A Theory
of Careers, Prentice Hall, 1973.
S., “A Hybrid Simulated Annealing for Solving an
Extended Model of Dynamic Cellular Manufacturing
System”, European Journal of Operational
Research, Vol. 185, pp. 563-592, 2008.
3. Balakrishnan, J., and Cheng, C. H., “Dynamic
Cellular Manufacturing under Multi Period Planning
Horizons”, Journal of Manufacturing Technology
Management, Vol. 16, No. 5, pp. 516-530, 2005.
4. Tavakkoli- Moghadam, R., Aryanezha, M. B., Safaei,
N., and Azaron, A., “Solving a Dynamic Cell
Formation Problem using Meta-Heuristics”, Applied
Mathematical and Computation, Vol. 170, pp 761-
780, 2005.
5. Defersha, F., and Chen, M., “A Comprehensive
Mathematical Model for the Design of Cellular
Manufacturing System”, International Journal of
Production Economics, Vol. 103, No. 2, pp. 767-783,
2006.
6. 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,
Vol. 184, pp. 721-728, 2007.
7. Saidi-Mehrabad, M., and Safaei, N., “A New Model
of Dynamic Cell Formation by a Neural Approach”,
International Journal Advance Manufacturing
Technology, Vol. 33, pp. 1001-1009, 2007.
8. Schaller, J., “Designing and Redesigning Cellular
Manufacturing System to Handle Demand Changes”,
Computer & Industrial Engineering, Vol. 53, pp.
478-490, 2007.
9. Defersha, F., and Chen, M., “A Parallel Genetic
Algorithm for Dynamic Cell Formation in Cellular
Manufacturing System”, International Journal of
Production Recearch, Vol. 46, No. 22, pp. 6389-
6413, 2008.
10. 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 and Mathematics with
Applications, Vol. 60, pp. 1014-1025, 2010.
11.Deljoo, V., Mirzapour Al-e-hashem S. M. J., Deljoo,
F., and Aryanezhad, M. B, “Using Genetic
Algorithm to Solve Dynamic Cell Formation
Problem”, Applied Mathematical Modeling, Vol. 34,
pp. 1078-1092, 2010.
12.Durán, O., Rodriguez, N., and Consalter, L. A.,
“Collaborative Particle Swarm Optimization with a
Data Mining Technique for Manufacturing Cell
Design”, Expert Systems with Applications Vol. 37,
pp. 1563-1567, 2010.
13. Ghotboddini, M. M., Rabbani, M., and Rahimian,
H., “A Comprehensive Dynamic Cell Formation
Design: Benders’ Decomposition Approach”, Expert
Systems with Applications, Vol. 38, pp. 2478-2488,
2011.
14.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
Flexible Reconfiguration by Simulated Annealing”,
Computers & Operations Research, Vol. 39, pp.
2642-2658, 2012.
15. Chen, M., and Dong C., “Coordinating Production
Planning in Cellular Manufacturing Environment
using Tabu Search”, Computers & Industrial
Engineering, Vol. 46, pp. 3571-588, 2004.
16. Simon, D., “Biogeography-based Optimization,
Evolutionary Computation”, IEEE Transactions,
Vol. 12, No. 6, pp. 702-713, 2008.
17.Holland, J. L., Making Vocational Choices, A Theory
of Careers, Prentice Hall, 1973.
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