Aplicación de la Búsqueda Armónica para el problema de formación de celdas de manufactura
Introducción: La manufactura celular (MC) es una aplicación de la tecnología de grupos que consiste en la agrupación de familias de productos y la formación de familias de máquinas, mediante la descomposición de un sistema de manufactura complejo en subsistemas que atienden las operaciones de famili...
- Autores:
-
Garavito Hernández, Edwin Alberto
Talero Sarmiento, Leonardo Hernán
Escobar Rodríguez, Laura Yeraldín
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/12226
- Palabra clave:
- harmony search
facility layout
cellular manufacturing
metaheuristic
búsqueda armónica
diseño de plantas
manufactura celular
metaheurísticas
- Rights
- openAccess
- License
- INGE CUC - 2019
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Aplicación de la Búsqueda Armónica para el problema de formación de celdas de manufactura |
| dc.title.translated.eng.fl_str_mv |
A Harmony Search approach for the Manufacturing Cell Design Problem |
| title |
Aplicación de la Búsqueda Armónica para el problema de formación de celdas de manufactura |
| spellingShingle |
Aplicación de la Búsqueda Armónica para el problema de formación de celdas de manufactura harmony search facility layout cellular manufacturing metaheuristic búsqueda armónica diseño de plantas manufactura celular metaheurísticas |
| title_short |
Aplicación de la Búsqueda Armónica para el problema de formación de celdas de manufactura |
| title_full |
Aplicación de la Búsqueda Armónica para el problema de formación de celdas de manufactura |
| title_fullStr |
Aplicación de la Búsqueda Armónica para el problema de formación de celdas de manufactura |
| title_full_unstemmed |
Aplicación de la Búsqueda Armónica para el problema de formación de celdas de manufactura |
| title_sort |
Aplicación de la Búsqueda Armónica para el problema de formación de celdas de manufactura |
| dc.creator.fl_str_mv |
Garavito Hernández, Edwin Alberto Talero Sarmiento, Leonardo Hernán Escobar Rodríguez, Laura Yeraldín |
| dc.contributor.author.spa.fl_str_mv |
Garavito Hernández, Edwin Alberto Talero Sarmiento, Leonardo Hernán Escobar Rodríguez, Laura Yeraldín |
| dc.subject.eng.fl_str_mv |
harmony search facility layout cellular manufacturing metaheuristic |
| topic |
harmony search facility layout cellular manufacturing metaheuristic búsqueda armónica diseño de plantas manufactura celular metaheurísticas |
| dc.subject.spa.fl_str_mv |
búsqueda armónica diseño de plantas manufactura celular metaheurísticas |
| description |
Introducción: La manufactura celular (MC) es una aplicación de la tecnología de grupos que consiste en la agrupación de familias de productos y la formación de familias de máquinas, mediante la descomposición de un sistema de manufactura complejo en subsistemas que atienden las operaciones de familias enteras de productos. Durante el presente trabajo se desarrolla un modelo de programación lineal entera que integra costos de producción con costos por transferencias entre celdas, además, se propone como método de solución un algoritmo denominado Búsqueda Armónica Objetivo: Determinar el desempeño de la Búsqueda Armónica modificada y las variantes de asignación de máquinas al problema de formación de celdas de manufactura. Metodología: Se desarrolla un modelo matemático de programación entera, el cual utiliza variables binarias para determinar la asignación de las operaciones de diversos productos a distintas máquinas en diferentes celdas, y variables enteras para cuantificar los requerimientos de máquinas y la cantidad de transferencias entre celdas. La validación del modelo se hace utilizando instancias modificadas de la literatura en el software GAMS usando el solver CPLEX y se desarrolla en MATLAB el algoritmo metaheurístico para dar solución aproximada. Resultados: Se encuentra que las variantes propuestas integradas en la Búsqueda Armónica logran buenos resultados aprovechando el enfoque de explotación del espacio de búsqueda. Conclusiones: A partir de las variantes aplicadas se logró encontrar muy buenas soluciones en tiempos considerablemente cortos; no obstante, es necesario implementar estrategias de exploración del espacio de búsqueda con el fin de evitar caer en óptimos locales. |
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2019 |
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2019-07-01 00:00:00 2024-04-09T20:15:21Z |
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2019-07-01 |
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Artículo de revista |
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Eng., vol. 51, no. 2, pp. 53–57, Feb. 1972. https://doi.org/10.1049/tpe.1972.0006 H. Seifoddini and P. M. Wolfe, “Application of the Similarity Coefficient Method in Group Technology,” IIE Trans., vol. 18, no. 3, pp. 271–277, Sep. 1986. https://doi.org/10.1080/07408178608974704 G. Srinivasan and T. T. Narendran, “GRAFICS-a nonhierarchical clustering algorithm for group technology,” Int. J. Prod. Res., vol. 29, no. 3, pp. 463–478, Mar. 1991. https://doi.org/10.1080/00207549108930083 M. Chandrasekharan and R. Rajagopalan, “ZODIAC-an algorithm for concurrent formation of part-families and machine-cells,” Int. J. Prod. Res., vol. 25, no. 6, pp. 835–850, Jun. 1987. https://doi.org/10.1080/00207548708919880 M. Chandrasekharan and R. Rajagopalan, “An ideal seed non-hierarchical clustering algorithm for cellular manufacturing,” Int. J. Prod. Res., vol. 24, no. 2, pp. 451–463, Mar. 1986. https://doi.org/10.1080/00207548608919741 A. Kusiak, “The generalized group technology concept,” Int. J. Prod. Res., vol. 25, no. 4, pp. 561–569, Apr. 1987. https://doi.org/10.1080/00207548708919861 S. Nicoletti, G. Nicosia and A. Pacifici, “Group Technology with Flow Shop Cells,” in Theory and Practice of Control and Systems. Singapur: World Scientific Publishing, 1999, pp. 800–804. https://doi.org/10.1142/9789814447317_0133 Z. Y. Lim, S. G. Ponnambalam and K. Izui, “Multi-objective hybrid algorithms for layout optimization in multi-robot cellular manufacturing systems,” Knowledge-Based Syst., vol. 120, pp. 87–98, Mar. 2017. https://doi.org/10.1016/j.knosys.2016.12.026 M. Diaby and A. L. Nsakanda, “Large-scale capacitated part-routing in the presence of process and routing flexibilities and setup costs,” J. Oper. Res. Soc., vol. 57, no. 9, pp. 1100–1112, Sep. 2006. https://doi.org/10.1057/palgrave.jors.2602072 H. Djellab and M. Gourgand, “A new heuristic procedure for the single-row facility layout problem,” Int. J. Comput. Integr. Manuf., vol. 14, no. 3, pp. 270–280, Jan. 2001. https://doi.org/10.1080/09511920010020721 M. Z. Allahyari and A. Azab, “A Novel Bi-level Continuous Formulation for the Cellular Manufacturing System Facility Layout Problem,” Procedia CIRP, vol. 33, pp. 87–92, Dec. 2015. https://doi.org/10.1016/j.procir.2015.06.017 S. P. Darla, C. D. Naiju, P. V. Sagar and B. Linkhit, “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,” Res. J. Appl. Sci. Eng. Technol., vol 7, no. 1, pp. 165–168, Jan. 2014. https://doi.org/10.19026/rjaset.7.235 M. Aghajani, A. Keramati, R. T. Moghadam and S. S. Mirjavadi, “A mathematical programming model for cellular manufacturing system controlled by kanban with rework consideration,” Int. J. Adv. Manuf. Technol., vol. 82, pp. 1377–1394, Mar. 2016. https://doi.org/10.1007/s00170-015-7635-8 Y. B. Sahin and S. 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Raju, “A Simulated Annealing Algorithm for Machine Cell Formation Under Uncertain Production Requirements,” AJSE, vol. 39, pp. 7345–7354, Aug. 2014. https://doi.org/10.1007/s13369-014-1306-1 C. Liu and J. Wang, “Cell formation and task scheduling considering multi-functional resource and part movement using hybrid simulated annealing,” Int. J. Comput. Intell. Syst., vol. 9, no. 4, pp. 765–777, Aug. 2016. https://doi.org/10.1080/18756891.2016.1204123 F. Shafigh, F. M. Defersha and S. Eid, “A linear programming embedded simulated annealing in the design of distributed layout with production planning and systems reconfiguration,” Int. J. Adv. Manuf. Technol., vol. 88, pp. 1119–1140, May. 2016. https://doi.org/10.1007/s00170-016-8813-z A. Iqbal and K. A. Al-Ghamdi, “Energy-efficient cellular manufacturing system: Eco-friendly revamping of machine shop configuration,” Energy, vol. 163, pp. 863–872, Nov. 2018. https://doi.org/10.1016/j.energy.2018.08.168 Y. Gholipour-Kanani, I. R. Tavakkoli-Moghaddam and I. A. Khorrami, “Solving a multi-criteria group scheduling problem for a cellular manufacturing system by scatter search,” JCIIE, vol. 28, no. 3, pp. 192–205, Feb. 2011. https://doi.org/10.1080/10170669.2010.549663 M. S. Jabal-Ameli and M. Moshref-Javadi, “Concurrent cell formation and layout design using scatter search,” Int. J. Adv. Manuf. Technol., vol. 71, pp. 1–22, Mar. 2014. https://doi.org/10.1007/s00170-013-5342-x A. S. Amiri and R. Ghodsi, “A variable neighborhood search method for an integrated cellular manufacturing systems with production planning and system reconfiguration,” in 4th Int. Conf. Math. Model. Comput. Simul., AMS2010, Bornea, Malaysia, 26-28 May. 2010, pp. 181–186. F. Jolai, M. Taghipour and B. Javadi, “A variable neighborhood binary particle swarm algorithm for cell layout problem,” Int. J. Adv. Manuf. Technol., vol. 55, no. 1-4, pp. 327–339, Jul. 2011. https://doi.org/10.1007/s00170-010-3039-y S.-H. Chung, T.-H. Wu and Ch.-Ch. Chang, “An efficient tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations,” Comput. Ind. Eng., vol. 60, no. 1, pp. 7–15, Feb. 2011. https://doi.org/10.1016/j.cie.2010.08.016 J. J. Du, G. X. Wang, Y. Yan and Q. Sang, “Tabu Search-based Formation of Reconfigurable Manufacturing Cells,” Appl. Mech. an Mater., vol. 397-400, no. 1, pp. 34–41, Sept. 2013. https://doi.org/10.4028/www.scientific.net/AMM.397-400.34 F. Sarayloo and R. Tavakkoli-Moghaddam, “Multi Objective Particle Swarm Optimization for a Dynamic Cell Formation Problem,” in World Congr. Eng. vol. III, WCE 2010, London, UK, Jun. 30 - Jul. 2, 2010. A. Azadeh, S. Pashapour and S. A. Zadeh, “Designing a cellular manufacturing system considering decision style, skill and job security by NSGA-II and response surface methodology,” Int. J. Prod. Res., vol. 54, no. 22, pp. 6825–6847, May. 2016. https://doi.org/10.1080/00207543.2016.1178407 K. S. Lee and Z. W. Geem, “A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice,” Comput. Methods Appl. Mech. Eng., vol. 194, no. 36-38, pp. 3902–3933, Sep. 2005. https://doi.org/10.1016/j.cma.2004.09.007 J. Arkat, M. H. Farahani and F. Ahmadizar, “Multi-objective genetic algorithm for cell formation problem considering cellular layout and operations scheduling,” Int. J. Comput. Integr. Manuf., vol. 25, no. 7, pp. 625–635, Jul. 2012. https://doi.org/10.1080/0951192X.2012.665182 M. Solimanpur, P. Vrat and R. Shankar, “Ant colony optimization algorithm to the inter-cell layout problem in cellular manufacturing,” Eur. J. Oper. Res., vol. 157, no. 3, pp. 592–606, Sep. 2004. https://doi.org/10.1016/S0377-2217(03)00248-0 A. J. Vakharia and Y.-L. Chang, “Cell formation in group technology: A combinatorial search approach,” Int. J. Prod. Res., vol. 35, no. 7, pp. 2025–2044, Jul. 1997. https://doi.org/10.1080/002075497195056 B. P. Ahmed, R. Tavakkoli-Moghaddam and N. Safaei, “A comparison of heuristic methods for solving a cellular manufacturing model in a dynamic environment,” UOW, Wolv., UK, WP007/04, 2004. F. S. Hillier and G. Lieberman, Introducción a la Investigación de Operaciones. 9 ed. México, D.F.: McGraw-Hill, 1997. Z. Yang, “Analysis and Design of Cellular Manufacturing Systems: Machine-Part Cell Formation and Operation Allocation,” Ph.D. dissertation, CWRU, Cleveland, Ohio, USA, 1995. D. M. Himmelblau, Applied Nonlinear Programming, 1st ed. NY, USA: McGraw-Hill, 1972. |
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Garavito Hernández, Edwin Alberto1ef186676685b44ad7d010ebdec03fa1300Talero Sarmiento, Leonardo Hernán2407e983123f522fbba55450e426847b300Escobar Rodríguez, Laura Yeraldínce934d19f847246aa73574e2fec33ba33002019-07-01 00:00:002024-04-09T20:15:21Z2019-07-01 00:00:002024-04-09T20:15:21Z2019-07-010122-6517https://hdl.handle.net/11323/12226https://doi.org/10.17981/ingecuc.15.2.2019.1510.17981/ingecuc.15.2.2019.152382-4700Introducción: La manufactura celular (MC) es una aplicación de la tecnología de grupos que consiste en la agrupación de familias de productos y la formación de familias de máquinas, mediante la descomposición de un sistema de manufactura complejo en subsistemas que atienden las operaciones de familias enteras de productos. Durante el presente trabajo se desarrolla un modelo de programación lineal entera que integra costos de producción con costos por transferencias entre celdas, además, se propone como método de solución un algoritmo denominado Búsqueda Armónica Objetivo: Determinar el desempeño de la Búsqueda Armónica modificada y las variantes de asignación de máquinas al problema de formación de celdas de manufactura. Metodología: Se desarrolla un modelo matemático de programación entera, el cual utiliza variables binarias para determinar la asignación de las operaciones de diversos productos a distintas máquinas en diferentes celdas, y variables enteras para cuantificar los requerimientos de máquinas y la cantidad de transferencias entre celdas. La validación del modelo se hace utilizando instancias modificadas de la literatura en el software GAMS usando el solver CPLEX y se desarrolla en MATLAB el algoritmo metaheurístico para dar solución aproximada. Resultados: Se encuentra que las variantes propuestas integradas en la Búsqueda Armónica logran buenos resultados aprovechando el enfoque de explotación del espacio de búsqueda. Conclusiones: A partir de las variantes aplicadas se logró encontrar muy buenas soluciones en tiempos considerablemente cortos; no obstante, es necesario implementar estrategias de exploración del espacio de búsqueda con el fin de evitar caer en óptimos locales.Introduction: Cellular Manufacturing (CM) is an application of group technology that consists of grouping components in part families and machines into cells, via the decomposition of a complex manufacturing system into small systems, which attend the operations of entire part families. In this work, we developed a linear programming model that integrates production costs with costs for transfers between cells. Besides, using an approach algorithm method called Harmony Search is solved the mathematical model. Objective: Evaluate the performance of the alternative Harmony Search and its machine allocation strategies in a cellular manufacturing problem. Method: The mathematical model consists in a linear programming structure in which there are binary variables to determine the assignment of the operations of products to different machines in diverse cells, and integer variables to count the requirements of machines and the number of transfers between cells. In order to validate the model, we use modified instances based on the literature review and set in GAMS software using the CPLEX solver, also, is developed a metaheuristic algorithm in MATLAB in order to give an approximate solution. Results:  The proposed Harmony Search and its variants can provide highlighted results taking advantage of the exploitation approach of the search space. Conclusions: The Harmony Search and its variants can provide outstanding solutions in considerably short times; nevertheless, it is necessary to implement strategies to explore the search space in order to avoid falling into local optima.application/pdftext/htmlapplication/xmlspaUniversidad de la CostaINGE CUC - 2019http://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistascientificas.cuc.edu.co/ingecuc/article/view/2120harmony searchfacility layoutcellular manufacturingmetaheuristicbúsqueda armónicadiseño de plantasmanufactura celularmetaheurísticasAplicación de la Búsqueda Armónica para el problema de formación de celdas de manufacturaA Harmony Search approach for the Manufacturing Cell Design ProblemArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Inge Cuc R. 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NY, USA: McGraw-Hill, 1972.167155215https://revistascientificas.cuc.edu.co/ingecuc/article/download/2120/2514https://revistascientificas.cuc.edu.co/ingecuc/article/download/2120/2551https://revistascientificas.cuc.edu.co/ingecuc/article/download/2120/2670Núm. 2 , Año 2019 : (Julio-Diciembre)OREORE.xmltext/xml2701https://repositorio.cuc.edu.co/bitstream/11323/12226/1/ORE.xmlaa0f95bc6f446e6251970de467dc0432MD51open access11323/12226oai:repositorio.cuc.edu.co:11323/122262024-04-09 15:15:21.791An error occurred on the license name.|||http://creativecommons.org/licenses/by-nc-nd/4.0metadata only accessRepositorio Universidad de La Costarepdigital@cuc.edu.co |