Management, Vol. 1, Issue 1, Dec  2017, Pages 28-49; DOI: 10.31058/j.mana.2017.11003 10.31058/j.mana.2017.11003

Developing a Multi-Objective Model on Cell Formation and Operator Assignment Based on Reliability and Workload Planning

Management, Vol. 1, Issue 1, Dec  2017, Pages 28-49.

DOI: 10.31058/j.mana.2017.11003

Kazem Amiryan 1 , Roozbeh Aziz Mohammadi 2 , Abbas Mahmoudabadi 3*

1 Department of industrial Engineering, Islamic Azad University, Shahre-Rey Branch, Iran

2 Department of industrial Engineering, Payame Noor University, Tehran, Iran

3 Department of industrial Engineering, MehrAstan University, Guilan, Iran

Received: 25 December 2017; Accepted: 10 January 2018; Published: 22 January 2018

Download PDF | Views 324 | Download 194


Human resources and maintenance are two main issues to reduce production cost. To reduce machines’ failures and repairing costs, they should be considered at the first stage of production planning. In many cases of cellular manufacturing system, designers assume that machines are always available but the failure time is a portion of machines lifecycles. In the present research work, a multi-objective mixed integer non-linear programming (MINLP) model has been developed for solving the cell formation and operator assignment problem in which machine reliability and workload balance have been considered, simultaneously. Operators’ selection and their assignment are also set based on the specified criteria and cross-training costs. The first level of objective function goes toward minimizing inter-intra cell movement, machines setup, operation, non-utilization, cross-training and operators salaries costs. The second level of objective function maximizes the system reliability which is defined as the total parts processing route reliabilities. The third level of objective function also minimizes the summation of standard deviation of machines activities times to balance machines workloads. In order to validate the proposed model, NSGA-II algorithm is applied to solve the numerical examples, and the Analytical Network Process (ANP) is also utilized for determining the most preferred solution from the extracted Pareto set. The performance of the NSGA-II is compared to NRGA algorithms to solve the problem using SAW method. Results revealed that increasing production cost reduces lead time but parts processing routes reliability is increased when reliability and workload balance are simultaneously considered in planning.


Cell Formation, Worker Assignment, Cross-Training, Reliability, Lognormal Distribution


© 2017 by the authors. Licensee International Technology and Science Press Limited. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


[1] Wemmerlov, U., Hyer, N.L. (1986). Procedures for the part family/machine group identification problem in cellular manufacture, Journal of Operation Management, 6, 125–147.
[2] 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.
[3] Bagheri, M., 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 Modeling, 38, 1237–1254.
[4] Agarwal, A., Sarkis, J. A. (1998), Review and analysis of comparative performance studies of functional and cellular manufacturing layouts. Computers and Industrial Engineering, 34(1), 77–89.
[5] Boughton, N. J., Arokiam, I. C. (2000), The application of the cellular manufacturing: A regional small to medium enterprise perspective. Proceedings of the Institution of Mechanical Engineers, 214(Part B), 751–754.
[6] Flynn, B. B., Jacobs, F. R. A. (1986), A simulation comparison of group technology with traditional job shop manufacturing. International Journal of Production Research, 24(5), 1171–1192.
[7] Morris, S. J. and Tersine, R. J. A. (1990), A simulation analysis of factors influencing the attractiveness of group technology cellular layouts. Management Science, 36(12), 1567–1578.
[8] Suresh, N. C., Meridith, J. R. (1994), Coping with the loss of pooling synergy in cellular manufacturing systems, Management Science, 40(4), 466–483.
[9] Eti, M.C., Ogaji, S.O.T., Probert, S.D., (2004), Implementing total productive maintenance in Nigerian manufacturing industries, Applied Energy, 79, 385–401.
[10] Das, K. (2008), A comparative study of exponential distribution vs Weibull distribution in machine reliability analysis in a CMS design. Computers and Industrial Engineering, 54, 12–33.
[11] Diallo, M., Perreval, H., Quillot, A. (2001), Manufacturing cell design with flexible routing capability in presence of unreliable machines. International Journal of Production Economics, 74(1–3), 175–182.
[12] Savsar M., (2000), Reliability analysis of flexible manufacturing cell, Reliability Engineering and System Safety, 67(2), 147–152.
[13] Yazhau, J., Molin, W., Zhixin, J. (1995), Probability failures of machining center failures. Reliability Engineering and System Safety, 50(1), 121–125.
[14] Bruecker, P. D., VandenBergh,J., Beliën, J., Demeulemeester, E. (2015), Work force planning incorporating skills: State of the art. European Journal of Operational Research, 243, 1–16.
[15] Chu C.H. (1989), Cluster analysis in manufacturing cellular formation. OMEGA, 17 (3) 289–295.
[16] Singh, N. (1993), Design of cellular manufacturing systems: an invited review, European Journal of Operation Research, 69 (3), 284–291.
[17] Chu, C.H. (1995), Recent advances in mathematical programming for cell formation, Manufacturing Research Technology, 24, 3-46.
[18] Papaioannou, G., Wilson, J. (2010), The evolution of cell formation problem methodologies based on recent studies (1997–2008): review and directions for future research. European Journal of Operation Research, 206(3), 509–521.
[19] Sakhaii M., Tavakkoli-MoghaddamR., Bagheri M.,Vatani B, (2015), A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines, Applied Mathematical Modeling.
[20] 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.
[21] Norman, B. A., Tharmmaphornphilas, W., Needy, K. L., Bidanda, B., Warner, R. C. (2002), Worker assignment in cellular manufacturing considering technical and human Journal of Production Research, 40(6), 1479-1492.
[22] Bidanda, B., Ariyawongrat, P., Needy, K. LaScola, Norman, B.A., Tharmmaphornphilas, W. (2005), Human related issues in manufacturing cell design, implementation, and operation: a review and survey, Computer and Industrial Engineering, 48, 507–523.
[23] Aryanezhad, M.B., Deljoo, V., Mirzapour Al-e-hashem, S.M.J. (2009), Dynamic cell formation and the worker assignment problem: a new model. International Journal of Advance Manufacturing Technology, 41 (3–4) 329–342.
[24] Mahdavi, I., Bootaki, B., Paydar, M. M. (2014), Manufacturing Cell Configuration Considering Worker Interest Concept Applying a Bi-Objective Programming Approach. International Journal of Industrial Engineering and Production Research, 25(1), 41-53.
[25] Ghotboddini, M.M., Rabbani, M., Rahimian, H. (2011), A comprehensive dynamic cell formation design: Benders’ decomposition approach, Expert System and Applied, 38 (3), 2478–2488.
[26] Azadeh, A., Sheikhalishahi, M., Koushan M. (2013), An integrated fuzzy DEA–Fuzzy simulation approach for optimization of operator allocation with learning effects in multi products CMS, Applied Mathematical Modeling, 37, 9922–9933.
[27] Mohammadi M., Forghani K. (2014), A novel approach for considering layout problem in cellular manufacturing systems with alternative processing routings and subcontracting approach. Applied Mathematical Modeling, 38(14), 3624–3640.
[28] Park, J., Bae, H., Dinh, T. C., Ryu, K. (2014), Operator allocation in cellular manufacturing systems by integrated genetic algorithm and fuzzy data envelopment analysis, International Journal of Advanced Manufacturing Technology, DOI 10.1007/s00170-014-6103-1.
[29] Mehdizadeh E., Rahimi V. (2016), An integrated mathematical model for solving dynamic cell formation problem considering operator assignment and inter/intra cell layouts. Applied Soft Computing, 42 (2016) 325–341.
[30] Zohrevand A.M., Rafiei H., Zohrevand A.H., (2016), Multi-objective dynamic cell formation problem: A stochastic programming approach. Computers & Industrial Engineering, 98 323–332.
[31] Sadeghi S., Seidi M., Shahbazi E., (2016), Impact of queuing theory and alternative process routings on machine busy time in a dynamic cellular manufacturing system, Journal of Industrial and Systems Engineering 9(2), 54-66.
[32] Mehdizadeh E., NiakiS. V. D., Rahimi V., (2016), A vibration damping optimization algorithm for solving a new multi-objective dynamic cell formation problem with workers training. Computers & Industrial Engineering, 101, 35–52.
[33] Chung, S. H., Wu, T. H., Chang, C. C. (2011), An efficient Tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations. Computers and Industrial Engineering, 60, 7–15.
[34] Rafiee, K., Rabbani, M., Rafiei, H., Rahimi-Vahed, A. (2011), A new approach towards integrated cell formation and inventory lot sizing in an unreliable cellular manufacturing system, Applied Mathematical Modelling, 35, 1810–1819.
[35] Jouzdani, J., Barzinpour, F., Shafia, M.A., Fathian, M. (2014), Applying simulated annealing to a generalization cell formation problem considering alternative routings and machine reliability, Asia-Pacific Journal of Operational Research, 31(4), 1450021 (26 pages).
[36] Kececioglu, D. B. (1991), Reliability engineering handbook. Vol. 1, DEStech publications.
[37] Seifoddini, H., Djassemi, M. (2001), The effect of reliability consideration on the application of quality index, Computer and industrial engineering, 40, 65-77.
[38] Deb, K., Samir, A., Amrit, P., Meyarivan, T. (2002), A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002; 6(2):103–12.
[39] Al Jadaan, O., Rajamani, L., Rao, C.R. (2009), Non-dominated ranked genetic algorithm for solving constrained multi-objective optimization problems. Journal of Theoretical and Applied Information Technology, 5, 714–725.
[40] Ishibuchi, H., Yoshida, T., Murata, T. (2003), Balance between genetic search and local search in memetic algorithms for multi-objective permutation flow-shop scheduling. IEEE Transactions Evolutionary Computation, 7, 204–223.
[41] Pan, Q. K., Wang, L., Qian, B. (2009), A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems. Computers and Operations Research, 36, 2498–2511.
[42] SaatyT.L., (2005), Theory and applications of the analytic network process. RWS Publications, Pittsburgh.