Document Type : Research Paper


1 Department of Industrial Engineering, University of Jordan, Amman, Jordan.

2 Department of Industrial Engineering, Hashemite University Zarqa, Jordan.


Cellular manufacturing is an important tool for manufacturing firms which leads to better productivity, focused and specialized manufacturing process. To utilize this important tool, the machines have to be grouped into cells. This work is related to using cellular manufacturing in a pharmaceutical factory with alternative routing. This adds more choices in the decision making process and presses for a better tool to make optimal selection. Several objectives may be considered to improve the productivity objectives such as the total number of exits and planning and scheduling robustness related objectives like bottleneck utilization and load balance between and within the alternative routes. Analytical hierarchical process (AHP) is used as a multi-objective decision making process to evaluate the best scenario amongst generated using simulation as a tool for modeling and evaluating the output for each scenario. Three customer case studies were considered with different preferences and the AHP evaluated the best scenario to fit these preferences. The best scenario can vary from one customer preferences to another but for the current system it turned out to be the same choice.


Main Subjects

[1] Offodile, O. F. (1991). Application of similarity coefficient method to parts coding and classification analysis in group technology. Journal of manufacturing systems10(6), 442-448.
[2] Mosier, C., & Taube, L. (1985). The facets of group technology and their impacts on implementation—a state-of-the-art survey. Omega13(5), 381-391.
[3]  Seifoddini, H., & Djassemi, M. (1995). Merits of the production volume based similarity coefficient in machine cell formation. Industrial technology, 26.
[4] Steudel, H. J., & Ballakur, A. (1987). A dynamic programming based heuristic for machine grouping in manufacturing cell formation. Computers & industrial engineering12(3), 215-222.
[5] King, J. R. (1980). Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm. International journal of production research18(2), 213-232.
[6]  King, J. R., & Nakornchai, V. (1982). Machine-component group formation in group technology: review and extension. The international journal of production research20(2), 117-133.
[7] Chandrasekharan, M., & Rajagopalan, R. (1986). An ideal seed non-hierarchical clustering algorithm for cellular manufacturing. International journal of production research24(2), 451-463.
[8] Chan, H. M., & Milner, D. A. (1982). Direct clustering algorithm for group formation in cellular manufacture. Journal of manufacturing systems1(1), 65-75.
[9]  McAuley, J. (1972). Machine grouping for efficient production. Production engineer51(2), 53-57. Seifoddini, H. (1990). Machine-component group analysis versus the similarity coefficient method in cellular manufacturing applications. Computers & industrial engineering18(3), 333-339.
[10]Seifoddini, H., & Wolfe, P. M. (1986). Application of similarity coefficient method in group technology cells. International journal of production research28, 293-300.
[11]Kumar, K. R., Kusiak, A., & Vannelli, A. (1986). Grouping of parts and components in flexible manufacturing systems. European journal of operational research24(3), 387-397.
[12] Gunasingh, K. R., & Lashkari, R. S. (1989). Machine grouping problem in cellular manufacturing systems—an integer programming approach. The international journal of production research27(9), 1465-1473.
[13] Han, C., & Ham, I. (1986). Multiobjective cluster analysis for part family formations. Journal of manufacturing Systems5(4), 223-230.
[14] Chu, C. H., & Hayya, J. C. (1991). A fuzzy clustering approach to manufacturing cell formation. The international journal of production research29(7), 1475-1487.
[15] Kaparthi, S., & Suresh, N. C. (1991). A neural network system for shape-based classification and coding of rotational parts. The international journal of production research29(9), 1771-1784.
[16] Davis, R., & Smith, R. G. (1983). Negotiation as a metaphor for distributed problem solving. Artificial intelligence20(1), 63-109.
[17]Smith, R. G. (1980). The contract net protocol: High-level communication and control in a distributed problem solver. IEEE transactions on computers, (12), 1104-1113.
[18]  Smith, R. G., & Davis, R. (1981). Frameworks for cooperation in distributed problem solving. IEEE Transactions on systems, man, and cybernetics11(1), 61-70.
[19]  Ben-Arieh, D., & Sreenivasan, R. (1999). Information analysis in a distributed dynamic group technology method. International Journal of Production Economics60, 427-432.
[20]  Ang, C. L., & Willey, P. C. T. (1984). A comparative study of the performance of pure and hybrid group technology manufacturing systems using computer simulation techniques. The international journal of production research22(2), 193-233.
[21]  Jensen, J. B., Malhotra, M. K., & Philipoom, P. R. (1996). Machine dedication and process flexibility in a group technology environment. Journal of Operations Management14(1), 19-39.
[22]  SONG, S. J., & HITOMI, K. (1996). Integrating the production planning and cellular layout for flexible cellular manufacturing. Production planning & control7(6), 585-593.
[23]  Wemmerlöv, U., & Hyer, N. L. (1987). Research issues in cellular manufacturing. International journal of production research25(3), 413-431.
[24]  Ho, Y. C., & Moodie, C. L. (1996). Solving cell formation problems in a manufacturing environment with flexible processing and routeing capabilities. International journal of production research34(10), 2901-2923.
[25]  Diallo, M., Pierreval, H., & Quilliot, A. (2001). Manufacturing cells design with flexible routing capability in presence of unreliable machines. International journal of production economics74(1), 175-182.
[26]  Seifoddini, H. (1990). A probabilistic model for machine cell formation. Journal of manufacturing systems9(1), 69-75.
[27]  Seifoddini, H., & Djassemi, M. (1996). Sensitivity analysis in cellular manufacturing system in the case of product mix variation. Computers & industrial engineering31(1), 163-167.
[28]  Brandimarte, P. (1999). Exploiting process plan flexibility in production scheduling: A multi-objective approach. European journal of operational research114(1), 59-71.
[29]  Chiang, T. C., & Lin, H. J. (2013). A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling. International journal of production economics141(1), 87-98.
[30]  Neto, A. R. P., & Gonçalves Filho, E. V. (2010). A simulation-based evolutionary multiobjective approach to manufacturing cell formation. Computers & industrial engineering59(1), 64-74.
[31]  Adil, G. K., Rajamani, D., & Strong, D. (1996). Cell formation considering alternate routeings. International journal of production research34(5), 1361-1380.
[32]  Wu, N. (1998). A concurrent approach to cell formation and assignment of identical machines in group technology. International journal of production research36(8), 2099-2114.
[33]  Zhao, C., & Wu, Z. (2000). A genetic algorithm for manufacturing cell formation with multiple routes and multiple objectives. International journal of production research38(2), 385-395.
[34]  Sofianopoulou, S. (1999). Manufacturing cells design with alternative process plans and/or replicate machines. International journal of production research37(3), 707-720.
[35]  Jayaswal, S., & Adil, G. K. (2004). Efficient algorithm for cell formation with sequence data, machine replications and alternative process routings. International journal of production research42(12), 2419-2433.
[36]   Dimopoulos, C. (2007). Explicit consideration of multiple objectives in cellular manufacturing. Engineering optimization39(5), 551-565.
[37]  Mollaghasemi, M., & Evans, G. W. (1994). Multicriteria design of manufacturing systems through simulation optimization. IEEE transactions on systems, man, and cybernetics24(9), 1407-1411.
[38]   Teleb, R., & Azadivar, F. (1994). A methodology for solvng multi-objective simulation-optimization problems. European journal of operational research72(1), 135-145.
[39]  Eskandari, H., Rabelo, L., & Mollaghasemi, M. (2005). Multiobjective simulation optimization using an enhanced genetic algorithm. Proceedings of the 2005 winter simulation conference (pp. 833–841). Doi:10.1109/WSC.2005.1574329
[40]  Rosen, S. L., Harmonosky, C. M., & Traband, M. T. (2008). Optimization of systems with multiple performance measures via simulation: Survey and recommendations. Computers & industrial engineering54(2), 327-339.
[41]  Weiss, E. N. (1987). Using the analytical process in a dynamic environment, Mathematical modelling, 9 (3-5), 211-216.