SIMULACIÓN Y OPTIMIZACIÓN DE SISTEMA DE MANUFACTURA CON CRITERIOS ECONÓMICO Y AMBIENTAL
DOI:
https://doi.org/10.22320/S07179103/2018.03Palabras clave:
Simulación, Optimización, Metaheurística, Toma de Decisiones Multicriterio, Impacto ambiental, Industria madereraResumen
En la simulación y optimización de un sistema de manufactura se buscan los parámetros de entrada para obtener el mejor desempeño del sistema, considerando en muchos casos un único objetivo o medida de desempeño. Sin embargo, muchas decisiones implican múltiples criterios, usualmente en conflicto. Actualmente, las empresas se ven exigidas a mejorar su rentabilidad, como también presionadas a reducir sus impactos en el ambiente y utilizar racionalmente la energía. En este trabajo se estudió el proceso de manufactura de artículos de madera de una pequeña empresa, para determinar alternativas de rediseño, con criterios económico y ambiental en simultáneo. Se construyó un modelo de simulación del sistema, el cual fue validado y verificado, y un modelo de optimización para el mismo, en el que se combinaron dos objetivos en una única función, incorporándose en la misma las preferencias de los decisores respecto a cada criterio. Los resultados, combinando ambos criterios en simultáneo, muestran substanciales mejoras en el beneficio, generando menos emisiones que si se persiguiese solamente el objetivo económico. Se concluye que la simulación en conjunto con la optimización multiobjetivo puede mejorar el desempeño económico-ambiental de los sistemas fabriles del sector maderero en forma apreciable, incluso en pequeños establecimientos.
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ANDERSON, R. B. Furniture rough mill costs evaluated by computer simulation. USDA Forest Service Research Paper NE- 518, 1983, 1-11.
ANDRADÓTTIR, S. A review of random search methods. En Handbook of simulation optimization. Springer: New York, 2015. pp. 277-292. ISBN 978-1-4939-1384-8.
AUNE, J. E. (1974). System simulation - a technique for sawmill productivity analyses and designs. The Forestry Chronicle, v.50, n.2, pp. 66-69.
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BAESLER, F. F., MORAGA, M. y RAMIS, F. J. Productivity improvement in the wood industry using simulation and artificial intelligence. En Proceedings of the 2002 Winter Simulation Conference. IEEE, 2002. pp. 1095-1098.
BANKS, J., CARSON, J. S., NELSON, B. L. y NICOL, D. M. Discrete-event system simulation. 4th ed. Pearson. 2005. ISBN 978013446793.
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CHOI, B. K. y KANG, D. Modeling and simulation of discrete-event systems. John Wiley & Sons: New Jersey, 2013. ISBN 978-1-118-38699-6.
CHWIF, L., BANKS, J., DE MOURA FILHO, J. P. y SANTINI, B. (2013). A framework for specifying a discrete-event simulation conceptual model. Journal of Simulation, v.7, n.1, 5060.
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ESMAEILIAN, B., BEHDAD, S. y WANG, B. (2016). The evolution and future of manufacturing: a review. Journal of Manufacturing Systems, v.39, n.1, pp.79-100.
FIGUEIRA, G. y ALMADA-LOBO, B. (2014). Hybrid simulation–optimization methods: a taxonomy and discussion. Simulation modelling practice and theory, v.46, pp.118–134.
FONTES, D. B. M. M. y GASPAR-CUNHA, . On multi-objective evolutionary algorithms. En Handbook of multicriteria analysis. Springer: Heidelberg, 2010.p. 287- 310. ISBN 978-3-54092827-0.
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GARCÍA, S., LUENGO, J. y HERRERA, F. Data preprocessing in data mining. Springer: Switzerland, 2015. ISBN 978-3-319-10246-7.
GARWOOD, T. L., HUGHES, B. R., OATES, M. R., O´CONNOR, D. y HUGHES, R. (2018). A review of energy simulation tools for the manufacturing sector. Renewable and Sustainable Energy Reviews, v. 81, pp. 895–911.
GUASCH, A., PIERA, M. À., CASANOVAS, J. y FIGUERAS, J. Modelado y simulación: aplicación a procesos logísticos de fabricación y servicios. 2ª ed. Edición UPC: Barcelona, 2003. ISBN 84-8301-704-0.
HEILALA, J., VATANEN, S., TONTERI, H., MONTONEN, J., LIND, S., JOHANSSON, B. y STAHRE, J. Simulation-based sustainable manufacturing system design. En Proceedings of the 2008 Winter Simulation Conference. IEEE, 2008, pp. 1922-1930.
HERRMANN, C., THIEDE, S., KARA, S. y HESSELBACH, J. (2011). Energy oriented simulation of manufacturing systems–concept and application. CIRP Annals-Manufacturing Technology, v.60, n.1, pp. 45-48.
HILLIER, F. S., y LIEBERMAN, G. J. Introduction to operations research. 10th ed. McGraw-Hill Education: New York, 2015. ISBN 978-0-07-352345-3.
HONG, L. J., NELSON, B. L. y XU, J. Discrete optimization via simulation. En Handbook of simulation optimization. Springer: New York, 2015. pp. 9-43. ISBN 978-1-4939-1383-1.
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JOHANSSON, M. T. y THOLLANDER, P. (2018). A review of barriers to and driving forces for improved energy efficiency in swedish industry–recommendations for successful in-house energy management. Renewable and Sustainable Energy Reviews, v.82, pp. 618-628.
KIBIRA, D. y SHAO, G. Integrating data mining and simulation optimization for decision making in manufacturing. En Applied simulation and optimization 2: new applications in logistics, industrial and aeronautical practice. Springer: Switzerland, 2017. pp. 81-105. ISBN 978-3-319-55809-7
KLEIJNEN, J. P. C. Design and analysis of simulation experiments. 2nd ed. Springer: Switzerland, 2015. ISBN 978-3-319-18086-1.
KLINE, D. E., WIEDENBECK, J. K. y ARAMAN, P. A. (1992). Management of wood products manufacturing using simulation/animation. Forest Products Journal, v.42, n.2, pp. 45-52.
LAGUNA, M. OptQuest: optimization of complex systems [en línea]. White paper, OptTek Systems Inc, 2011 [Consulta: 8 de febrero de 2018]. Disponible en: https://goo.gl/2rStef.
LAGUNA, M. y MARKLUND, J. Business process modeling, simulation and design. 2nd ed. CRC Press: Boca Raton, 2013. ISBN 978-1-4398-8528-4.
LAROSE, D. T. y LAROSE, C. D. Data mining and predictive analytics. 2nd ed. John Wiley & Sons: New Jersey, 2015. ISBN 978-1-118-11619-7.
LAW, A. M. How to build valid and credible simulation models. En Proceedings of the 2008 Winter Simulation Conference. IEEE, 2008. pp. 39-47.
LAW, A. M. A tutorial on design of experiments for simulation modeling. En Proceedings of the 2014 Winter Simulation Conference. IEEE, 2014. pp. 66-80.
LAW, A. M. Simulation modeling and analysis. 5th ed. McGraw-Hill Education: New York, 2015a. ISBN 978-0-07-340132-4.
LAW, A. M. Statistical analysis of simulation output data: the practical state of the art. En: Proceedings of the 2015 Winter Simulation Conference. IEEE, 2015b, pp. 1810-1819.
LIN, R., SIR, M. Y. y PASUPATHY, K. S. (2013). Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: specific application to determining optimal resource levels in surgical services. Omega, v.41, n.5, pp. 881-892.
Ministerio de Energía y Minería de Argentina. Cálculo del factor de emisión de CO2 de la Red Argentina de Energía Eléctrica ©2016 [Consulta: 8 de febrero de 2018]. Disponible en: https://goo.gl/Hqq5jZ.
MOHAMAD, I. B. y USMAN, D. (2013). Standardization and its effects on k-means clustering algorithm. Research Journal of Applied Sciences, Engineering and Technology, v.6, n.17, p. 3299-3303.
MOURTZIS, D., DOUKAS, M. y BERNIDAKI, D. (2014). Simulation in manufacturing: review and challenges. Procedia CIRP, v.25, pp. 213-229.
NEGAHBAN, A. y SMITH, J. S. (2014). Simulation for manufacturing system design and operation: literature review and analysis. Journal of Manufacturing Systems, v.33, n.2, pp. 241-261.
NELSON, B.L. Foundations and methods of stochastic simulation: a first course. Springer Science & Business Media: New York, 2013. ISBN 978-1-4614-6159-3.
OMOGBAI, O. y SALONITIS, K. (2016). Manufacturing system lean improvement design using discrete event simulation. Procedia CIRP, v.57, pp.195-200. OptTek. OptQuest: the world’s leading simulation optimization engine ©2018 [en línea] [Consulta: 8 de febrero de 2018]. Disponible en: https://goo.gl/HrWjYv.
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PAWLEWSKI, P. y BORUCKI, J. “Green” possibilities of simulation software for production and logistics – a survey. En Information Technologies in environmental engineering: new trends and challenges. Springer: Berlin, Heidelberg, 2011. p. 675- 688. ISBN 978-3-642-19535-8.
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ANDERSON, R. B. Furniture rough mill costs evaluated by computer simulation. USDA Forest Service Research Paper NE- 518, 1983, 1-11.
ANDRADÓTTIR, S. A review of random search methods. En Handbook of simulation optimization. Springer: New York, 2015. pp. 277-292. ISBN 978-1-4939-1384-8.
AUNE, J. E. (1974). System simulation - a technique for sawmill productivity analyses and designs. The Forestry Chronicle, v.50, n.2, pp. 66-69.
BAESLER, F. F. y SEPÚLVEDA, J. A. Multi-response simulation optimization using stochastic genetic search within a goal programming framework. En Proceedings of the 2000 Winter Simulation Conference. IEEE, 2000. pp. 788–794.
BAESLER, F. F., MORAGA, M. y RAMIS, F. J. Productivity improvement in the wood industry using simulation and artificial intelligence. En Proceedings of the 2002 Winter Simulation Conference. IEEE, 2002. pp. 1095-1098.
BANKS, J., CARSON, J. S., NELSON, B. L. y NICOL, D. M. Discrete-event system simulation. 4th ed. Pearson. 2005. ISBN 978013446793.
BETTER, M., GLOVER, F. y KOCHENBERGER, G. Simulation optimization: improving decisions under uncertainty. En Breakthroughs in decision science and risk analysis. John Wiley & Sons: New Jersey, 2015. p. 59-81. ISBN 978-1-118-21716.
CHICA, M., JUAN, A. A., CORDÓN, Ó. y KELTON, W. D. Why simheuristics? Benefits, limitations, and best practices when combining metaheuristics with simulation. SSRN[en línea]. 1 de enero de 2017 [Consulta: 8 de febrero de 2018]. Disponible en: Chica, http:// dx.doi.org/10.2139/ssrn.2919208.
CHOI, B. K. y KANG, D. Modeling and simulation of discrete-event systems. John Wiley & Sons: New Jersey, 2013. ISBN 978-1-118-38699-6.
CHWIF, L., BANKS, J., DE MOURA FILHO, J. P. y SANTINI, B. (2013). A framework for specifying a discrete-event simulation conceptual model. Journal of Simulation, v.7, n.1, 5060.
DEB, K. Multi-objective Optimization. In: E. K. BURKE and G. Kendall, eds. Search methodologies. 2nd ed. Springer: Boston, 2014. p. 403-449. ISBN 978-1-4614-6939-1.
DIAZ-ELSAYED, N., JONDRAL, A., GREINACHER, S., DORNFELD, D. y LANZA, G. (2013). Assessment of lean and green strategies by simulation of manufacturing systems in discrete production environments. CIRP Annals - Manufacturing Technology, v.62, pp. 475–478.
ELARBI, M., BECHIKH, S., SAID, L. B. y DATTA, R. Multi-objective optimization: classical and evolutionary approaches. En Recent advances in evolutionary multi-objective optimization. Springer: Switzerland, 2017. p. 1-30. ISBN 978-3-319-42977-9.
ELDER, M. DES view on simulation modeling: SIMUL8. En Discrete-event simulation and system dynamics for management decision making. John Wiley & Sons: United Kingdom, 2014. pp. 199-214. ISBN 978-1-118-34902-1.
ELIZANDRO, D. y TAHA, H. Performance evaluation of industrial systems: discrete event simulation in using Excel/VBA. 2nd ed. CRC Press: Boca Raton, 2012. ISBN 978-1-43987135-5.
ESMAEILIAN, B., BEHDAD, S. y WANG, B. (2016). The evolution and future of manufacturing: a review. Journal of Manufacturing Systems, v.39, n.1, pp.79-100.
FIGUEIRA, G. y ALMADA-LOBO, B. (2014). Hybrid simulation–optimization methods: a taxonomy and discussion. Simulation modelling practice and theory, v.46, pp.118–134.
FONTES, D. B. M. M. y GASPAR-CUNHA, . On multi-objective evolutionary algorithms. En Handbook of multicriteria analysis. Springer: Heidelberg, 2010.p. 287- 310. ISBN 978-3-54092827-0.
FRANCHETTI, M. J. y APUL, D. Carbon footprint analysis: concepts, methods, implementation, and case studies. CRC Press: Boca Raton, 2013. ISBN 978-1-4398-5784-7.
FU, M. C. (2002). Optimization for simulation: theory vs. practice. INFORMS Journal on Computing, v.14, n.3, pp.192-215.
FU, M. C. Handbook of simulation optimization. Springer: New York, 2015. ISBN 978-1-49391383-1.
FU, M. C., GLOVER, F. W. y APRIL, J. Simulation optimization: a review, new developments, and applications. En Proceedings of the 2005 Winter Simulation Conference. IEEE, 2005. pp. 1- 14.
FU, M. C., BAYRAKSAN, G., HENDERSON, S. G., NELSON, B. L., POWELL, W. B., RYZHOV, I. O. y THENGVALL, B. Simulation optimization: a panel on the state of the art in research and practice. En Proceedings of the 2014 Winter Simulation Conference. IEEE, 2014. p. 3696- 3706.
GARCÍA, S., LUENGO, J. y HERRERA, F. Data preprocessing in data mining. Springer: Switzerland, 2015. ISBN 978-3-319-10246-7.
GARWOOD, T. L., HUGHES, B. R., OATES, M. R., O´CONNOR, D. y HUGHES, R. (2018). A review of energy simulation tools for the manufacturing sector. Renewable and Sustainable Energy Reviews, v. 81, pp. 895–911.
GUASCH, A., PIERA, M. À., CASANOVAS, J. y FIGUERAS, J. Modelado y simulación: aplicación a procesos logísticos de fabricación y servicios. 2ª ed. Edición UPC: Barcelona, 2003. ISBN 84-8301-704-0.
HEILALA, J., VATANEN, S., TONTERI, H., MONTONEN, J., LIND, S., JOHANSSON, B. y STAHRE, J. Simulation-based sustainable manufacturing system design. En Proceedings of the 2008 Winter Simulation Conference. IEEE, 2008, pp. 1922-1930.
HERRMANN, C., THIEDE, S., KARA, S. y HESSELBACH, J. (2011). Energy oriented simulation of manufacturing systems–concept and application. CIRP Annals-Manufacturing Technology, v.60, n.1, pp. 45-48.
HILLIER, F. S., y LIEBERMAN, G. J. Introduction to operations research. 10th ed. McGraw-Hill Education: New York, 2015. ISBN 978-0-07-352345-3.
HONG, L. J., NELSON, B. L. y XU, J. Discrete optimization via simulation. En Handbook of simulation optimization. Springer: New York, 2015. pp. 9-43. ISBN 978-1-4939-1383-1.
HUNTER, S. R., APPLEGATE, E. A., ARORA, V., CHONG, B., COOPER, K., RINCÓNGUEVARA, O. y VIVAS-VALENCIA, C. (2017). An introduction to multi-objective simulation optimization. Optimization online [en línea], [Consulta: 8 de febrero de 2018]. Disponible en: https://goo.gl/NXDsrx.
JOHANSSON, M. T. y THOLLANDER, P. (2018). A review of barriers to and driving forces for improved energy efficiency in swedish industry–recommendations for successful in-house energy management. Renewable and Sustainable Energy Reviews, v.82, pp. 618-628.
KIBIRA, D. y SHAO, G. Integrating data mining and simulation optimization for decision making in manufacturing. En Applied simulation and optimization 2: new applications in logistics, industrial and aeronautical practice. Springer: Switzerland, 2017. pp. 81-105. ISBN 978-3-319-55809-7
KLEIJNEN, J. P. C. Design and analysis of simulation experiments. 2nd ed. Springer: Switzerland, 2015. ISBN 978-3-319-18086-1.
KLINE, D. E., WIEDENBECK, J. K. y ARAMAN, P. A. (1992). Management of wood products manufacturing using simulation/animation. Forest Products Journal, v.42, n.2, pp. 45-52.
LAGUNA, M. OptQuest: optimization of complex systems [en línea]. White paper, OptTek Systems Inc, 2011 [Consulta: 8 de febrero de 2018]. Disponible en: https://goo.gl/2rStef.
LAGUNA, M. y MARKLUND, J. Business process modeling, simulation and design. 2nd ed. CRC Press: Boca Raton, 2013. ISBN 978-1-4398-8528-4.
LAROSE, D. T. y LAROSE, C. D. Data mining and predictive analytics. 2nd ed. John Wiley & Sons: New Jersey, 2015. ISBN 978-1-118-11619-7.
LAW, A. M. How to build valid and credible simulation models. En Proceedings of the 2008 Winter Simulation Conference. IEEE, 2008. pp. 39-47.
LAW, A. M. A tutorial on design of experiments for simulation modeling. En Proceedings of the 2014 Winter Simulation Conference. IEEE, 2014. pp. 66-80.
LAW, A. M. Simulation modeling and analysis. 5th ed. McGraw-Hill Education: New York, 2015a. ISBN 978-0-07-340132-4.
LAW, A. M. Statistical analysis of simulation output data: the practical state of the art. En: Proceedings of the 2015 Winter Simulation Conference. IEEE, 2015b, pp. 1810-1819.
LIN, R., SIR, M. Y. y PASUPATHY, K. S. (2013). Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: specific application to determining optimal resource levels in surgical services. Omega, v.41, n.5, pp. 881-892.
Ministerio de Energía y Minería de Argentina. Cálculo del factor de emisión de CO2 de la Red Argentina de Energía Eléctrica ©2016 [Consulta: 8 de febrero de 2018]. Disponible en: https://goo.gl/Hqq5jZ.
MOHAMAD, I. B. y USMAN, D. (2013). Standardization and its effects on k-means clustering algorithm. Research Journal of Applied Sciences, Engineering and Technology, v.6, n.17, p. 3299-3303.
MOURTZIS, D., DOUKAS, M. y BERNIDAKI, D. (2014). Simulation in manufacturing: review and challenges. Procedia CIRP, v.25, pp. 213-229.
NEGAHBAN, A. y SMITH, J. S. (2014). Simulation for manufacturing system design and operation: literature review and analysis. Journal of Manufacturing Systems, v.33, n.2, pp. 241-261.
NELSON, B.L. Foundations and methods of stochastic simulation: a first course. Springer Science & Business Media: New York, 2013. ISBN 978-1-4614-6159-3.
OMOGBAI, O. y SALONITIS, K. (2016). Manufacturing system lean improvement design using discrete event simulation. Procedia CIRP, v.57, pp.195-200. OptTek. OptQuest: the world’s leading simulation optimization engine ©2018 [en línea] [Consulta: 8 de febrero de 2018]. Disponible en: https://goo.gl/HrWjYv.
PATEL, K. M. A. y THAKRAL, P. The best clustering algorithms in data mining. En International Conference on Communication and Signal Processing. IEEE, 2016. p. 2042-2046.
PAWLEWSKI, P. y BORUCKI, J. “Green” possibilities of simulation software for production and logistics – a survey. En Information Technologies in environmental engineering: new trends and challenges. Springer: Berlin, Heidelberg, 2011. p. 675- 688. ISBN 978-3-642-19535-8.
REYNOLDS, H. W. y GATCHELL, C. J. Sawmill simulation: concepts and computer use. USDA Forest Service Research Note NE- 100, 1969, 1-5.
RÍOS INSÚA, D., RÍOS INSÚA, S., MARTÍN JIMÉNEZ, J. y JIMÉNEZ MARTÍN, A. Simulación. Métodos y aplicaciones. 2ª ed. Alfaomega: México, 2009. ISBN 978-970-15-1457-3.
SARGENT, R. G. An introductory tutorial on verification and validation of simulation models. En Proceedings of the 2015 Winter Simulation Conference. IEEE, pp. 1729-1740. (2015)
SIMUL8 Corporation. SIMUL8 simulation software ©2018 [en línea] [Consulta: 8 de febrero de 2018]. Disponible en: https://goo.gl/xU5FDB.
SOLDING, P. Increased energy efficiency in manufacturing systems using discrete event simulation: applied studies on the Swedish foundry industry [en línea]. Doctoral thesis. Leicester: De Montfort University, 2008. [Consulta: 8 de febrero de 2018]. Disponible en: https://goo.gl/6i9kLh.
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2018-04-20
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