Keywords
Cellular manufacturing
optimization
discrete-event simulation
analytic network process
optimization
discrete-event simulation
analytic network process
How to Cite
Cellular manufacturing system selection with multi-lean measures using optimization and simulation. (2016). Ingenieria Y Universidad, 21(1), 7-25. https://doi.org/10.11144/Javeriana.iyu21-1.dcms
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Abstract
Introduction: this study proposes a method to design and balance a cellular manufacturing system of a typical industrial company to obtain an optimal configuration in terms of the process, total cost, idle time and reliability criteria. Methods: the developed method has three phases. The first phase obtains candidate solutions using optimization models to minimize the cycle time and total cost. In the second phase, the performance measures for the remaining criteria of each candidate solution are found using discrete-event simulation. In the last phase, the optimal configuration is selected using the analytic network process (ANP). Results: the proposed method was validated with a practical case, where the optimal configuration had the best reliability with a zero-smoothness index, which minimized the wasted time and excess inventory. However, it was not the configuration with the lowest cost. Conclusions: This method has two contributing elements: multiple lean criteria and the approach, which combines different solution strategies to select the best configuration in an integral manner.
[1] R. Shah and P. T. Ward, “Defining and developing measures of lean production,” vol. 25, pp. 785–805, 2007.
[2] M. Dora and D. Van Goubergen, “Application of lean practices in small and medium-sized food enterprises,” vol. 116, no. 1, pp. 125–141, 2012.
[3] A. Azadegan, P. C. Patel, A. Zangoueinezhad, and K. Linderman, “The effect of environmental complexity and environmental dynamism on lean practices,” J. Oper. Manag., vol. 31, no. 4, pp. 193–212, 2013.
[4] M. D. Regan, The Kaizen Revolution How to use kaizen events to double your profits. Raleigh, North Carolina: Holden Press, 2000.
[5] L. Rivera and F. Frank Chen, “Measuring the impact of Lean tools on the cost–time investment of a product using cost–time profiles,” Robot. Comput. Integr. Manuf., vol. 23, no. 6, pp. 684–689, 2007.
[6] A. Scholl and C. Becker, “State-of-the-art exact and heuristic solution procedures for simple assembly line balancing,” Eur. J. Oper. Res., vol. 168, no. 3, pp. 666–693, 2006.
[7] U. Saif, Z. Guan, B. Wang, J. Mirza, and S. Huang, “A survey on assembly lines and its types,” Front. Mech. Eng., vol. 9, no. 2, pp. 95–105, 2014.
[8] N. Kriengkorakot and N. Pianthong, “The assembly line balancing problem: Review Problem,” J. Ind. Eng., vol. 6, no. 3, pp. 18–25, 1955.
[9] S. Ghosg and R. J. Gagnon, “A comprehensive literature review and analysis of the design, balancing and scheduling of assembly systems,” Int. J. Prod. Res., vol. 24, no. 4, pp. 637–670, 1989.
[10] A. Arroyo and O. Rubiano, “Solving a Two-Sided Assembly Line Balancing Problem using Memetic,” vol. 13, no. 2, pp. 267–280, 2009.
[11] C. Becker and A. Scholl, “A survey on problems and methods in generalized assembly line balancing,” Eur. J. Oper. Res., vol. 168, no. 3, pp. 694–715, 2006.
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[13] B. Rekiek and A. Delchambre, Assembly Line Design the Balancing of Mixed-Model Hybrid Assembly Lines with Genetic Algorithms. Berlin: Springer, 2006.
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[15] R. Esmaeilbeigi, B. Naderi, and P. Charkhgard, “The type E simple assembly line balancing problem: A mixed integer linear programming formulation,” Comput. Oper. Res., vol. 64, pp. 168–177, 2015.
[16] R. Ramezanian and A. Ezzatpanah, “Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem,” Comput. Ind. Eng., vol. 87, pp. 74–80, 2015.
[17] I. Mahdavi, A. Aalaei, M. M. Paydar, and M. Solimanpur, “Designing a mathematical model for dynamic cellular manufacturing systems considering production planning and worker assignment,” Comput. Math. with Appl., vol. 60, no. 4, pp. 1014–1025, 2010.
[18] P. T. Zacharia and A. C. Nearchou, “A population-based algorithm for the bi-objective assembly line worker assignment and balancing problem,” Eng. Appl. Artif. Intell., vol. 49, pp. 1–9, 2016.
[19] M. H. Alavidoost, H. Babazadeh, and S. T. Sayyari, “An interactive fuzzy programming approach for bi-objective straight and U-shaped assembly line balancing problem,” Appl. Soft Comput. J., vol. 40, pp. 221–235, 2016.
[20] P. Samouei, P. Fattahi, J. Ashayeri, and S. Ghazinoory, “Bottleneck easing-based assignment of work and product mixture determination: Fuzzy assembly line balancing approach,” Appl. Math. Model., vol. 40, no. 7–8, pp. 4323–4340, 2016.
[21] W. Zhang, W. Xu, and M. Gen, “Hybrid Multiobjective Evolutionary Algorithm for Assembly Line Balancing Problem with Stochastic Processing Time,” Procedia Comput. Sci., vol. 36, no. 3, pp. 587–592, 2014.
[22] M. Chica, J. Bautista, Ó. Cordón, and S. Damas, “A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand,” Omega, vol. 58, pp. 55–68, 2016.
[23] C. R. Shiyas and V. Madhusudanan Pillai, “A mathematical programming model for manufacturing cell formation to develop multiple configurations,” J. Manuf. Syst., vol. 33, no. 1, pp. 149–158, 2014.
[24] J. Rezaeian, N. Javadian, R. Tavakkoli-Moghaddam, and F. Jolai, “A hybrid approach based on the genetic algorithm and neural network to design an incremental celular manufacturing system,” Appl. Soft Comput. J., vol. 11, no. 6, pp. 4195–4202, 2011.
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[27] B. Bootaki, I. Mahdavi, and M. M. Paydar, “New criteria for configuration of cellular manufacturing considering product mix variation,” Comput. Ind. Eng., vol. 98, pp. 413–426, 2016.
[28] A. Delgoshaei and C. Gomes, “A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost,” Appl. Soft Comput., vol. 49, pp. 27–55, 2016.
[29] G. W. Evans and S. M. Alexander, “Proceedings of the 2007 Winter Simulation Conference S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, eds.,” pp. 1615–1623, 2007.
[30] R. Al-Aomar, “Handling multi-lean measures with simulation and simulated annealing,” J. Franklin Inst., vol. 348, no. 7, pp. 1506–1522, 2011.
[31] S. Seifermann, J. Böllhoff, J. Metternich, and A. Bellaghnach, “Evaluation of work measurement concepts for a cellular manufacturing reference line to enable low cost automation for lean machining,” Procedia CIRP, vol. 17, pp. 588–593, 2014.
[32] N. Manavizadeh, N. S. Hosseini, M. Rabbani, and F. Jolai, “A Simulated Annealing algorithm for a mixed model assembly U-line balancing type-I problem considering human efficiency and Just-In-Time approach,” Comput. Ind. Eng., vol. 64, no. 2, pp. 669–685, 2013.
[33] A. M. Deif, “Dynamic analysis of a lean cell under uncertainty,” Int. J. Prod. Res., vol. 50, no. 4, pp. 1127–1139, 2012.
[34] W. J. Hopp and M. L. Spearman, Factory Physics, 2nd ed. New York: McGraw-Hill, 2001.
[35] M. A. Kabir and M. T. Tabucanon, “Batch-model assembly line balancing: A multiattribute decision making approach,” Int. J. Prod. Econ., vol. 41, no. 1–3, pp. 193–201, 1995.
[36] N. Suresh Kumar and R. Sridharan, “Simulation modelling and analysis of part and tool flow control decisions in a flexible manufacturing system,” Robot. Comput. Integr. Manuf., vol. 25, no. 4–5, pp. 829–838, 2009.
[37] G. Shambu and N. C. Suresh, “Performance of hybrid cellular manufacturing systems: a computer simulation investigation,” Eur. J. Oper. Res., vol. 120, no. 2, pp. 436–458, 2000.
[38] F. Al-Mubarak, C. Canel, and B. M. Khumawala, “A simulation study of focused celular manufacturing as an alternative batch-processing layout,” Int. J. Prod. Econ., vol. 83, no. 2, pp. 123–138, 2003.
[39] A. R. Pitombeira Neto and E. V. Gonçalves Filho, “A simulation-based evolutionary multiobjective approach to manufacturing cell formation,” Comput. Ind. Eng., vol. 59, no. 1, pp. 64–74, 2010.
[40] L. Tiacci, “Event and object oriented simulation to fast evaluate operational objectives of mixed model assembly lines problems,” Simul. Model. Pract. Theory, vol. 24, pp. 35–48, 2012.
[41] A. R. Mendes, A. L. Ramos, A. S. Simaria, and P. M. Vilarinho, “Combining heuristic procedures and simulation models for balancing a PC camera assembly line,” Comput. Ind. Eng., vol. 49, no. 3, pp. 413–431, 2005.
[42] P. Cappanera, F. Visintin, and C. Banditori, “Comparing resource balancing criteria in master surgical scheduling: A combined optimization-simulation approach,” Int. J. Prod. Econ., vol. 158, pp. 179–196, 2014.
[43] I. Kucukkoc and D. Z. Zhang, “Balancing of parallel U-shaped assembly lines,” Comput. Oper. Res., vol. 64, pp. 233–244, 2015.
[44] H. Goken, K. Agpak, and R. Benzer, “Balancing of parallel assembly lines,” Int. J. Prod. Econ., vol. 103, no. 2, pp. 600–609, 2006.
[45] Black J.T and D. T. Phillips, Lean Engineering the Future Has Arrived, College Station, TX, USA: Virtualbookworm, 2014.
[46] A. Aghajani, S. A. Didehbani, M. Zadahmad, M. H. Seyedrezaei, and O. Mohsenian, “A multi-objective mathematical model for cellular manufacturing systems design with probabilistic demand and machine reliability analysis,” Int. J. Adv. Manuf. Technol., vol. 75, no. 5, pp. 755–770, 2014.
[47] U. Bahalke, K. Dolatkhahi, H. Dehghani, E. Jahani, V. Yazdanparast, and H. Hajihosseini, “Formulation and heuristic algorithm for flow time minimization in a simple assembly line,” Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., vol. 226, no. 3, pp. 512–526, 2012.
[48] R. E. Gunther, G. D. Johnson, and R. S. Peterson, “Currently practiced formulations for the assembly line balance problem,” J. Oper. Manag., vol. 3, no. 4, pp. 209–221, 1983.
[49] J. Bukchin and J. Rubinovitz, “A weighted approach for assembly line design with station paralleling and equipment selection,” IIE Trans., vol. 35, no. 1, pp. 73–85, 2003.
[50] T. L. Saaty, “Fundamentals of the analytic network process,” Kobe, Japan, p. 14, Aug- 1999.
[51] T. L. Saaty, “Decision making with the analytic hierarchy process,” Int. J. Serv. Sci., vol. 1, no. 1, p. 83, 2008.
[52] F. R. Sempere, C. I. Miralles, C. A. Romano, and E. V. Salort, Tiempos, aplicación de mejora de métodos de trabajo y medición de tiempos. México: Limusa, 2008.
[53] T. L. Saaty, “How to make a decision: The analytic hierarchy process,” Eur. J. Oper. Res., vol. 48, no. 1, pp. 9–26, 1990.
[54] W. Grzechca, “Methodology for cost oriented Introduction of assembly line balancing problem Basic assumption of assembly line balancing problem,” Total Logist. Manag., no. 4, pp. 57–67, 2011.
[55] W. Grzechca, “Assembly Line Balancing Problem with Reduced Number of Workstations,” in 19th World Congress the International Federation of Automatic Control, 2014, pp. 6180–6185.
[2] M. Dora and D. Van Goubergen, “Application of lean practices in small and medium-sized food enterprises,” vol. 116, no. 1, pp. 125–141, 2012.
[3] A. Azadegan, P. C. Patel, A. Zangoueinezhad, and K. Linderman, “The effect of environmental complexity and environmental dynamism on lean practices,” J. Oper. Manag., vol. 31, no. 4, pp. 193–212, 2013.
[4] M. D. Regan, The Kaizen Revolution How to use kaizen events to double your profits. Raleigh, North Carolina: Holden Press, 2000.
[5] L. Rivera and F. Frank Chen, “Measuring the impact of Lean tools on the cost–time investment of a product using cost–time profiles,” Robot. Comput. Integr. Manuf., vol. 23, no. 6, pp. 684–689, 2007.
[6] A. Scholl and C. Becker, “State-of-the-art exact and heuristic solution procedures for simple assembly line balancing,” Eur. J. Oper. Res., vol. 168, no. 3, pp. 666–693, 2006.
[7] U. Saif, Z. Guan, B. Wang, J. Mirza, and S. Huang, “A survey on assembly lines and its types,” Front. Mech. Eng., vol. 9, no. 2, pp. 95–105, 2014.
[8] N. Kriengkorakot and N. Pianthong, “The assembly line balancing problem: Review Problem,” J. Ind. Eng., vol. 6, no. 3, pp. 18–25, 1955.
[9] S. Ghosg and R. J. Gagnon, “A comprehensive literature review and analysis of the design, balancing and scheduling of assembly systems,” Int. J. Prod. Res., vol. 24, no. 4, pp. 637–670, 1989.
[10] A. Arroyo and O. Rubiano, “Solving a Two-Sided Assembly Line Balancing Problem using Memetic,” vol. 13, no. 2, pp. 267–280, 2009.
[11] C. Becker and A. Scholl, “A survey on problems and methods in generalized assembly line balancing,” Eur. J. Oper. Res., vol. 168, no. 3, pp. 694–715, 2006.
[12] W.-M. Chow, Assembly Line Design: Methodology and Applications (Manufacturing Engineering and Materials Processing). New York: Marcell Decker, 1990.
[13] B. Rekiek and A. Delchambre, Assembly Line Design the Balancing of Mixed-Model Hybrid Assembly Lines with Genetic Algorithms. Berlin: Springer, 2006.
[14] J. Bukchin and M. Masin, “Multi-objective design of team oriented assembly systems,” Eur. J. Oper. Res., vol. 156, no. 2, pp. 326–352, 2004.
[15] R. Esmaeilbeigi, B. Naderi, and P. Charkhgard, “The type E simple assembly line balancing problem: A mixed integer linear programming formulation,” Comput. Oper. Res., vol. 64, pp. 168–177, 2015.
[16] R. Ramezanian and A. Ezzatpanah, “Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem,” Comput. Ind. Eng., vol. 87, pp. 74–80, 2015.
[17] I. Mahdavi, A. Aalaei, M. M. Paydar, and M. Solimanpur, “Designing a mathematical model for dynamic cellular manufacturing systems considering production planning and worker assignment,” Comput. Math. with Appl., vol. 60, no. 4, pp. 1014–1025, 2010.
[18] P. T. Zacharia and A. C. Nearchou, “A population-based algorithm for the bi-objective assembly line worker assignment and balancing problem,” Eng. Appl. Artif. Intell., vol. 49, pp. 1–9, 2016.
[19] M. H. Alavidoost, H. Babazadeh, and S. T. Sayyari, “An interactive fuzzy programming approach for bi-objective straight and U-shaped assembly line balancing problem,” Appl. Soft Comput. J., vol. 40, pp. 221–235, 2016.
[20] P. Samouei, P. Fattahi, J. Ashayeri, and S. Ghazinoory, “Bottleneck easing-based assignment of work and product mixture determination: Fuzzy assembly line balancing approach,” Appl. Math. Model., vol. 40, no. 7–8, pp. 4323–4340, 2016.
[21] W. Zhang, W. Xu, and M. Gen, “Hybrid Multiobjective Evolutionary Algorithm for Assembly Line Balancing Problem with Stochastic Processing Time,” Procedia Comput. Sci., vol. 36, no. 3, pp. 587–592, 2014.
[22] M. Chica, J. Bautista, Ó. Cordón, and S. Damas, “A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand,” Omega, vol. 58, pp. 55–68, 2016.
[23] C. R. Shiyas and V. Madhusudanan Pillai, “A mathematical programming model for manufacturing cell formation to develop multiple configurations,” J. Manuf. Syst., vol. 33, no. 1, pp. 149–158, 2014.
[24] J. Rezaeian, N. Javadian, R. Tavakkoli-Moghaddam, and F. Jolai, “A hybrid approach based on the genetic algorithm and neural network to design an incremental celular manufacturing system,” Appl. Soft Comput. J., vol. 11, no. 6, pp. 4195–4202, 2011.
[25] P. Chutima and P. Chimklai, “Multi-objective two-sided mixed-model assembly line balancing using particle swarm optimisation with negative knowledge,” Comput. Ind. Eng., vol. 62, no. 1, pp. 39–55, 2012.
[26] Z. Yuguang, A. Bo, and Z. Yong, “A PSO algorithm for multi-objective hull assembly line balancing using the stratified optimization strategy,” Comput. Ind. Eng., vol. 98, pp. 53–62, 2016.
[27] B. Bootaki, I. Mahdavi, and M. M. Paydar, “New criteria for configuration of cellular manufacturing considering product mix variation,” Comput. Ind. Eng., vol. 98, pp. 413–426, 2016.
[28] A. Delgoshaei and C. Gomes, “A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost,” Appl. Soft Comput., vol. 49, pp. 27–55, 2016.
[29] G. W. Evans and S. M. Alexander, “Proceedings of the 2007 Winter Simulation Conference S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, eds.,” pp. 1615–1623, 2007.
[30] R. Al-Aomar, “Handling multi-lean measures with simulation and simulated annealing,” J. Franklin Inst., vol. 348, no. 7, pp. 1506–1522, 2011.
[31] S. Seifermann, J. Böllhoff, J. Metternich, and A. Bellaghnach, “Evaluation of work measurement concepts for a cellular manufacturing reference line to enable low cost automation for lean machining,” Procedia CIRP, vol. 17, pp. 588–593, 2014.
[32] N. Manavizadeh, N. S. Hosseini, M. Rabbani, and F. Jolai, “A Simulated Annealing algorithm for a mixed model assembly U-line balancing type-I problem considering human efficiency and Just-In-Time approach,” Comput. Ind. Eng., vol. 64, no. 2, pp. 669–685, 2013.
[33] A. M. Deif, “Dynamic analysis of a lean cell under uncertainty,” Int. J. Prod. Res., vol. 50, no. 4, pp. 1127–1139, 2012.
[34] W. J. Hopp and M. L. Spearman, Factory Physics, 2nd ed. New York: McGraw-Hill, 2001.
[35] M. A. Kabir and M. T. Tabucanon, “Batch-model assembly line balancing: A multiattribute decision making approach,” Int. J. Prod. Econ., vol. 41, no. 1–3, pp. 193–201, 1995.
[36] N. Suresh Kumar and R. Sridharan, “Simulation modelling and analysis of part and tool flow control decisions in a flexible manufacturing system,” Robot. Comput. Integr. Manuf., vol. 25, no. 4–5, pp. 829–838, 2009.
[37] G. Shambu and N. C. Suresh, “Performance of hybrid cellular manufacturing systems: a computer simulation investigation,” Eur. J. Oper. Res., vol. 120, no. 2, pp. 436–458, 2000.
[38] F. Al-Mubarak, C. Canel, and B. M. Khumawala, “A simulation study of focused celular manufacturing as an alternative batch-processing layout,” Int. J. Prod. Econ., vol. 83, no. 2, pp. 123–138, 2003.
[39] A. R. Pitombeira Neto and E. V. Gonçalves Filho, “A simulation-based evolutionary multiobjective approach to manufacturing cell formation,” Comput. Ind. Eng., vol. 59, no. 1, pp. 64–74, 2010.
[40] L. Tiacci, “Event and object oriented simulation to fast evaluate operational objectives of mixed model assembly lines problems,” Simul. Model. Pract. Theory, vol. 24, pp. 35–48, 2012.
[41] A. R. Mendes, A. L. Ramos, A. S. Simaria, and P. M. Vilarinho, “Combining heuristic procedures and simulation models for balancing a PC camera assembly line,” Comput. Ind. Eng., vol. 49, no. 3, pp. 413–431, 2005.
[42] P. Cappanera, F. Visintin, and C. Banditori, “Comparing resource balancing criteria in master surgical scheduling: A combined optimization-simulation approach,” Int. J. Prod. Econ., vol. 158, pp. 179–196, 2014.
[43] I. Kucukkoc and D. Z. Zhang, “Balancing of parallel U-shaped assembly lines,” Comput. Oper. Res., vol. 64, pp. 233–244, 2015.
[44] H. Goken, K. Agpak, and R. Benzer, “Balancing of parallel assembly lines,” Int. J. Prod. Econ., vol. 103, no. 2, pp. 600–609, 2006.
[45] Black J.T and D. T. Phillips, Lean Engineering the Future Has Arrived, College Station, TX, USA: Virtualbookworm, 2014.
[46] A. Aghajani, S. A. Didehbani, M. Zadahmad, M. H. Seyedrezaei, and O. Mohsenian, “A multi-objective mathematical model for cellular manufacturing systems design with probabilistic demand and machine reliability analysis,” Int. J. Adv. Manuf. Technol., vol. 75, no. 5, pp. 755–770, 2014.
[47] U. Bahalke, K. Dolatkhahi, H. Dehghani, E. Jahani, V. Yazdanparast, and H. Hajihosseini, “Formulation and heuristic algorithm for flow time minimization in a simple assembly line,” Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., vol. 226, no. 3, pp. 512–526, 2012.
[48] R. E. Gunther, G. D. Johnson, and R. S. Peterson, “Currently practiced formulations for the assembly line balance problem,” J. Oper. Manag., vol. 3, no. 4, pp. 209–221, 1983.
[49] J. Bukchin and J. Rubinovitz, “A weighted approach for assembly line design with station paralleling and equipment selection,” IIE Trans., vol. 35, no. 1, pp. 73–85, 2003.
[50] T. L. Saaty, “Fundamentals of the analytic network process,” Kobe, Japan, p. 14, Aug- 1999.
[51] T. L. Saaty, “Decision making with the analytic hierarchy process,” Int. J. Serv. Sci., vol. 1, no. 1, p. 83, 2008.
[52] F. R. Sempere, C. I. Miralles, C. A. Romano, and E. V. Salort, Tiempos, aplicación de mejora de métodos de trabajo y medición de tiempos. México: Limusa, 2008.
[53] T. L. Saaty, “How to make a decision: The analytic hierarchy process,” Eur. J. Oper. Res., vol. 48, no. 1, pp. 9–26, 1990.
[54] W. Grzechca, “Methodology for cost oriented Introduction of assembly line balancing problem Basic assumption of assembly line balancing problem,” Total Logist. Manag., no. 4, pp. 57–67, 2011.
[55] W. Grzechca, “Assembly Line Balancing Problem with Reduced Number of Workstations,” in 19th World Congress the International Federation of Automatic Control, 2014, pp. 6180–6185.
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