Vol. 11 Núm. 1 (2020): Revista Aglala
Artículos Cientificos

Formación de celdas de manufacturas dinámicas para la toma de decisiones en el diseño de instalaciones industriales: una revisión

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Laura Y. Escobar Rodríguez
Universidad Industrial de Santander
Edwin A. Garavito Hernández
Universidad Industrial de Santander
Leonardo H. Talero Sarmiento
Universidad Industrial de Santander

Publicado 2020-06-03

Palabras clave

  • Celdas de manufactura dinámicas,
  • planeación y diseño de instalaciones,
  • problema de formación de celdas de manufactura,
  • corporativo

Cómo citar

Escobar Rodríguez, L. Y., Garavito Hernández, E. A., & Talero Sarmiento, L. H. (2020). Formación de celdas de manufacturas dinámicas para la toma de decisiones en el diseño de instalaciones industriales: una revisión. Aglala, 11(1), 169–184. Recuperado a partir de https://revistas.uninunez.edu.co/index.php/aglala/article/view/1570
Aviso de derechos de autor/a

Resumen

Este artículo presenta una revisión de literatura con el fin de caracterizar el Problema de Formación de Celdas de Manufactura Dinámicas, realizando la identificación de los criterios de optimización, las principales restricciones consideradas y los métodos de solución más usados. Para ello, se condujo una adaptación de la declaración PRISMA para revisiones sistemáticas, en conjunto con una metodología bola de nieve para la selección de los estudios a analizar; la búsqueda de documentos se realiza en las bases de datos Web of Science y Scopus, considerando una ventana de tiempo entre 2007 y 2019. Como resultados generales, se encuentra que la minimización de costos es el criterio de optimización utilizado con mayor frecuencia y que las restricciones usualmente consideradas están asociadas a la secuencia de operaciones, averías de máquinas, y variación del tamaño de lote de procesamiento. De otra parte, considerando la naturaleza altamente combinatoria de los problemas de optimización revisados, se encuentra que los métodos de solución metaheurísticos utilizados en mayor medida son algoritmos genéticos, y recocido simulado. Finalmente, se determinan tres tendencias de investigación primero, la incorporación de dos o más criterios de optimización en una formulación matemática; segundo, el desarrollo e implementación de algoritmos metaheurísticos híbridos y; tercero, la evaluación de los métodos de solución existentes a partir de problemas de referencia o aplicaciones industriales reales.

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Citas

  1. Aghajani, A., Ahmadi Didehbani, S., Zadahmad, M., Hasan Seyedrezaei, M., & Mohsenian, O. (2014). A multi-objective mathematical model for cellular manufacturing systems design with probabilistic demand and machine reliability analysis. International Journal of Advanced Manufacturing Technology, 75, 755–770. https://doi.org/10.1007/s00170-014-6084-0
  2. Aghajani, M., Keramati, A., Moghadam, R. T., & Mirjavadi, S. S. (2016). A mathematical programming model for cellular manufacturing system controlled by kanban with rework consideration. International Journal of Advanced Manufacturing Technology, 83(5–8), 1377–1394. https://doi.org/10.1007/s00170-015-7635-8
  3. Ah kioon, S., Bulgak, A. A., & Bektas, T. (2009). Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration. European Journal of Operational Research, 192(2), 414–428. https://doi.org/10.1016/j.ejor.2007.09.023
  4. Ahkioon, S., Bulgak, A. A., & Bektas, T. (2009). Cellular manufacturing systems design with routing flexibility, machine procurement, production planning and dynamic system reconfiguration. International Journal of Production Research, 47(6), 1573–1600. https://doi.org/10.1080/00207540701581809
  5. Amiri, A. S., & Ghodsi, R. (2010). A variable neighborhood search method for an integrated cellular manufacturing systems with production planning and system reconfiguration. AMS2010: Asia Modelling Symposium 2010 - 4th International Conference on Mathematical Modelling and Computer Simulation, 181–186. https://doi.org/10.1109/AMS.2010.47
  6. Arıkan, F., & Güngör, Z. (2009). Modeling of a manufacturing cell design problem with fuzzy multi-objective parametric programming. Mathematical and Computer Modelling, 50(3–4), 407–420. https://doi.org/10.1016/j.mcm.2009.04.017
  7. Aryanezhad, M., Deljoo, V., & Mirzapour Al-e-hashem, S. (2009). Dynamic cell formation and the worker assignment problem: a new model. The International Journal of Advanced Manufacturing Technology, 41(3–4), 329–342. https://doi.org/10.1007/s00170-008-1479-4
  8. Azadeh, A., Pashapour, S., & Abdolhossein Zadeh, S. (2016). Designing a cellular manufacturing system considering decision style, skill and job security by NSGA-II and response surface methodology. International Journal of Production Research, 54(22), 6825–6847. https://doi.org/10.1080/00207543.2016.1178407
  9. Bajestani, M. A., Rabbani, M., Rahimi-Vahed, A. R., & Khoshkhou, G. B. (2009). A multi-objective scatter search for a dynamic cell formation problem. Computers & Operations Research, 36, 777–794. https://doi.org/10.1016/j.cor.2007.10.026
  10. 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.
  11. Boulif, M., & Atif, K. (2008). A new fuzzy genetic algorithm for the dynamic bi-objective cell formation problem considering passive and active strategies. International Journal of Approximate Reasoning. https://doi.org/10.1016/j.ijar.2007.03.003
  12. Chang, C., Wu, T., & Wu, C. (2013). An efficient approach to determine cell formation , cell layout and intracellular machine sequence in cellular manufacturing systems. Computers & Industrial Engineering, 66(2), 438–450. https://doi.org/10.1016/j.cie.2013.07.009
  13. Chung, S., Wu, T., & Chang, C. (2011). An efficient tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations q. Computers & Industrial Engineering, 60(1), 7–15. https://doi.org/10.1016/j.cie.2010.08.016
  14. Darla, S. P., Naiju, C. D., & Sagar, P. V. (2014). Optimization of Inter Cellular Movement of Parts in Cellular Manufacturing System Using Genetic Algorithm Optimization of Inter Cellular Movement of Parts in Cellular Manufacturing System Using Genetic Algorithm. January. https://doi.org/10.19026/rjaset.7.235
  15. Defersha, F., & Chen, M. (2008). A parallel genetic algorithm for dynamic cell formation in cellular manufacturing systems. International Journal of Production Research, 46(22), 6389–6413. https://doi.org/10.1080/00207540701441962
  16. Deljoo, V., Mirzapour Al-e-hashem, S. M. J., Deljoo, F., & Aryanezhad, M. B. (2010). Using genetic algorithm to solve dynamic cell formation problem. Applied Mathematical Modelling. https://doi.org/10.1016/j.apm.2009.07.019
  17. Du, J., Wang, G., Yan, Y., & Sang, Q. (2013). Tabu Search-based Formation of Reconfigurable Manufacturing Cells Jingjun Du 1,. Applied Mechanics an Materials, 400(1), 34–41. https://doi.org/10.4028/www.scientific.net/AMM.397-400.34
  18. Fan, J., & Cao, M. (2013). Study on the quasi-dynamic cell-formation problem. Journal of Applied Sciences. https://doi.org/10.3923/jas.2013.2813.2818
  19. Fan, J., & Feng, D. (2013). Design of cellular manufacturing system with quasi-dynamic dual resource using multi-objective GA. International Journal of Production Research. https://doi.org/10.1080/00207543.2012.748228
  20. Feng, H., Xi, L., Xia, T., & Pan, E. (2018). Concurrent cell formation and layout design based on hybrid approaches. Applied Soft Computing Journal, 66, 346–359. https://doi.org/10.1016/j.asoc.2018.02.021
  21. Garavito-Hernandez, E. A., Talero-Sarmiento, L. H., & Escobar-Rodriguez, L. Y. (2019). Aplicación de la Búsqueda Armónica para el problema de formación de celdas de manufactura. INGE CUC, 15(2), 155–167. https://doi.org/10.17981/ingecuc.15.2.2019.15
  22. Gholipour-kanani, Y., Tavakkoli-moghaddam, R., & Khorrami, A. (2011). Solving a multi-criteria group scheduling problem for a cellular manufacturing system by scatter search. 0669. https://doi.org/10.1080/10170669.2010.549663
  23. Iqbal, A., & Al-Ghamdi, K. A. (2018). Energy-efficient cellular manufacturing system: Eco-friendly revamping of machine shop configuration. Energy, 163, 863–872. https://doi.org/10.1016/j.energy.2018.08.168
  24. Jabal-Ameli, M. S., & Moshref-Javadi, M. (2015). Concurrent cell formation and layout design using scatter search. International Journal of Advanced Manufacturing Technology, 71(1–4), 1–22. https://doi.org/10.1007/s00170-013-5342-x
  25. Javadian, N., Aghajani, A., Rezaeian, J., & Ghaneian Sebdani, M. J. (2011). A multi-objective integrated cellular manufacturing systems design with dynamic system reconfiguration. International Journal of Advanced Manufacturing Technology, 56, 307–317. https://doi.org/10.1007/s00170-011-3164-2
  26. Jayakumar, V., & Raju, R. (2010). An adaptive cellular manufacturing system design with routing flexibility and dynamic system reconfiguration. European Journal of Scientific Research, 47(4), 595–611.
  27. Jayakumar, V., & Raju, R. (2014). A Simulated Annealing Algorithm for Machine Cell Formation Under Uncertain Production Requirements. Arabian Journal for Science and Engineering, 39(10), 7345–7354. https://doi.org/10.1007/s13369-014-1306-1
  28. Jolai, F., Taghipour, M., & Javadi, B. (2011). A variable neighborhood binary particle swarm algorithm for cell layout problem. 327–339. https://doi.org/10.1007/s00170-010-3039-y
  29. Kia, R., Baboli, A., Javadian, N., Tavakkoli-Moghaddam, R., Kazemi, M., & Khorrami, J. (2012). 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 and Operations Research. https://doi.org/10.1016/j.cor.2012.01.012
  30. Liu, C., & Wang, J. (2016). Cell formation and task scheduling considering multi-functional resource and part movement using hybrid simulated annealing. International Journal of Computational Intelligence Systems, 9(4), 765–777. https://doi.org/10.1080/18756891.2016.1204123
  31. Liu, C., Wang, J., & Leung, J. Y. T. (2018). Integrated bacteria foraging algorithm for cellular manufacturing in supply chain considering facility transfer and production planning. Applied Soft Computing Journal, 62, 602–618. https://doi.org/10.1016/j.asoc.2017.10.034
  32. Maleki, R., Ketabi, S., & Rafiei, F. M. (2018). Grouping both machines and parts in cellular technology by Genetic Algorithm. Journal of Industrial and Production Engineering, 1015, 1–11. https://doi.org/10.1080/21681015.2017.1411402
  33. Mehdizadeh, E., Daei Niaki, S. V., & Rahimi, V. (2016). A vibration damping optimization algorithm for solving a new multi-objective dynamic cell formation problem with workers training. Computers and Industrial Engineering. https://doi.org/10.1016/j.cie.2016.08.012
  34. Niakan, F., Baboli, A., Moyaux, T., & Botta-Genoulaz, V. (2016). A new multi-objective mathematical model for dynamic cell formation under demand and cost uncertainty considering social criteria. Applied Mathematical Modelling. https://doi.org/10.1016/j.apm.2015.09.047
  35. Rabbani, M., Farrokhi-Asl, H., Rafiei, H., & Khaleghi, R. (2017). Using metaheuristic algorithms to solve a dynamic cell formation problem with consideration of intra-cell layout design. Intelligent Decision Technologies, 11, 109–126. https://doi.org/10.3233/IDT-160281
  36. Rheault, M., Drolet, J. R., & Abdulnour, G. (1995). Physically reconfigurable virtual cells: A dynamic model for a highly dynamic environment. Computers and Industrial Engineering, 29(1–4), 221–225. https://doi.org/10.1016/0360-8352(95)00075-C
  37. Saeidi, S., Solimanpur, M., & Mahdavi, I. (2014). A multi-objective genetic algorithm for solving cell formation problem using a fuzzy goal programming approach. 1635–1652. https://doi.org/10.1007/s00170-013-5392-0
  38. Safaei, N., Saidi-Mehrabad, M., Tavakkoli-Moghaddam, R., & Sassani, F. (2008). A fuzzy programming approach for a cell formation problem with dynamic and uncertain conditions. Fuzzy Sets and Systems, 159, 215–236. https://doi.org/10.1016/j.fss.2007.06.014
  39. Sahin, Y. B., & Alpay, S. (2016). A metaheuristic approach for a cubic cell formation problem. Expert Systems With Applications, 65, 40–51. https://doi.org/10.1016/j.eswa.2016.08.034
  40. Saidi-Mehrabad, M., & Safaei, N. (2007). A new model of dynamic cell formation by a neural approach. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-006-0518-2
  41. Sarayloo, F., & Tavakkoli-Moghaddam, R. (2010a). Imperialistic competitive algorithm for solving a dynamic cell formation problem with production planning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-642-14922-1_34
  42. Sarayloo, F., & Tavakkoli-Moghaddam, R. (2010b). Multi Objective Particle Swarm Optimization for a Dynamic Cell Formation Problem. World Congress of Engineering, 3.
  43. Satoglu, S. I., Durmusoglu, M. B., & Ertay, T. (2010). A mathematical model and a heuristic approach for design of the hybrid manufacturing systems to facilitate one-piece flow. 7543. https://doi.org/10.1080/00207540903089544
  44. Saxena, L. K., & Jain, P. K. (2011). Dynamic cellular manufacturing systems design—a comprehensive model. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-010-2842-9
  45. Shafigh, F., Defersha, F. M., & Moussa, S. E. (2017). A linear programming embedded simulated annealing in the design of distributed layout with production planning and systems reconfiguration. International Journal of Advanced Manufacturing Technology, 88(1–4), 1119–1140. https://doi.org/10.1007/s00170-016-8813-z
  46. Shahdi-Pashaki, S., Teymourian, E., & Tavakkoli-Moghaddam, R. (2018). New approach based on group technology for the consolidation problem in cloud computing-mathematical model and genetic algorithm. Computational and Applied Mathematics, 37(1), 693–718. https://doi.org/10.1007/s40314-016-0362-4
  47. Soolaki, M. (2012). A multi-objective integrated cellular manufacturing systems design with production planning, worker assignment and dynamic system reconfiguration. International Journal of Industrial and Systems Engineering. https://doi.org/10.1504/IJISE.2012.049412
  48. Subhaa, R., & Natarajan, J. (2018). Service level-based production smoothening model for robust cellular manufacturing system. International Journal of Advanced Manufacturing Technology, 94(1–4), 475–496. https://doi.org/10.1007/s00170-017-0863-3
  49. Tavakkoli-Moghaddam, R., Minaeian, S., & Rabbani, M. (2007). A new multi-objective model for dynamic cell formation problem with fuzzy parameters. IJE Transactions A: Basics, 21(2).
  50. Tavakkoli-Moghaddam, R., Rahimi-Vahed, A. R., Ghodratnama, A., & Siadat, A. (2009). A simulated annealing method for solving a new mathematical model of a multi-criteria cell formation problem with capital constraints. Advances in Engineering Software. https://doi.org/10.1016/j.advengsoft.2008.04.008
  51. Tavakkoli-Moghaddam, R., Safaei, N., & Sassani, F. (2008). A new solution for a dynamic cell formation problem with alternative routing and machine costs using simulated annealing. Journal of the Operational Research Society. https://doi.org/10.1057/palgrave.jors.2602436
  52. Tavakkoli, R., Ranjbar, M., Amin, G. R., & Siadat, A. (2012). A cell formation problem considering machine utilization. 1127–1139. https://doi.org/10.1007/s10845-010-0395-2
  53. Torkul, O., & Ustun, O. (2015). Proposal of a nonlinear multi-objective genetic algorithm using conic scalarization to the design of cellular manufacturing systems. 30–57. https://doi.org/10.1007/s10696-014-9194-y
  54. Urrutia, G., & Bonfill, X. (2011). Declaración PRISMA : una propuesta para mejorar la publicaciónn de revisiones sistemáticas y metaanálisis. Medicina Clínica, 135(11), 507–511. https://doi.org/10.1016/j.medcli.2010.01.015
  55. Vafaeinezhad, M., Kia, R., & Shahnazari-Shahrezaei, P. (2016). Robust optimization of a mathematical model to design a dynamic cell formation problem considering labor utilization. Journal of Industrial Engineering International, 12, 45–60. https://doi.org/10.1007/s40092-015-0127-5
  56. Venkatadri, U., Elaskari, S. M., & Kurdi, R. (2017). A multi-commodity network flow-based formulation for the multi-period cell formation problem. International Journal of Advanced Manufacturing Technology, 91, 175–187. https://doi.org/10.1007/s00170-016-9673-2
  57. Wang, X., Tang, J., & Yung, K.-L. (2009). Optimization of the multi-objective dynamic cell formation problem using a scatter search approach. International Journal of Advanced Manufacturing Technology, 44, 318–329. https://doi.org/10.1007/s00170-008-1835-4
  58. Wang, X., Tang, J., & Yung, K. (2010). A scatter search approach with dispatching rules for a joint decision of cell formation and parts scheduling in batches. 7543. https://doi.org/10.1080/00207540902922828
  59. Xiaoqing, W., Jiafu, T., Jun, G., & Mei, C. (2008). A Nonlinear Multi-Objective Model of Dynamic Cell Formation. Chinese Control Ad Decision Conference.
  60. 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. https://doi.org/10.1016/j.cie.2016.03.026