Multi-agent system for steel manufacturing process

This work was carried out in the company ACINOX Las Tunas, Cuba, to design an integrated automation architecture based on intelligent agents for control, monitoring, and decision-making in the production process that guarantees an improvement in planning and management of the process in the steelwor...

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Autores:: Ricardo Rodríguez, Angel Raúl
Benítez, Israel F
González Yero, Guillermo
Núñez Alvarez, José Ricardo

Tipo de recurso:: Article of journal

Fecha de publicación:: 2022

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

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repository_id_str
dc.title.eng.fl_str_mv	Multi-agent system for steel manufacturing process
title	Multi-agent system for steel manufacturing process
spellingShingle	Multi-agent system for steel manufacturing process Agents Artificial intelligence Decision support systems Integrated manufacturing Intelligent control
title_short	Multi-agent system for steel manufacturing process
title_full	Multi-agent system for steel manufacturing process
title_fullStr	Multi-agent system for steel manufacturing process
title_full_unstemmed	Multi-agent system for steel manufacturing process
title_sort	Multi-agent system for steel manufacturing process
dc.creator.fl_str_mv	Ricardo Rodríguez, Angel Raúl Benítez, Israel F González Yero, Guillermo Núñez Alvarez, José Ricardo
dc.contributor.author.spa.fl_str_mv	Ricardo Rodríguez, Angel Raúl Benítez, Israel F González Yero, Guillermo Núñez Alvarez, José Ricardo
dc.subject.proposal.eng.fl_str_mv	Agents Artificial intelligence Decision support systems Integrated manufacturing Intelligent control
topic	Agents Artificial intelligence Decision support systems Integrated manufacturing Intelligent control
description	This work was carried out in the company ACINOX Las Tunas, Cuba, to design an integrated automation architecture based on intelligent agents for control, monitoring, and decision-making in the production process that guarantees an improvement in planning and management of the process in the steelwork plant. The great differences of technologies and systems of each steel mill and the multiple restrictions, methods, and techniques, within a wide dynamic strongly concatenated, do not generalize automation systems feasibly. In our research, we use international research results and the experience of the plant technologists to create three levels of distributed intelligent architecture: business, production planning-control, and steel manufacturing. Each level manages to integrate and balance the particular and general interests for efficient decision-making combined between hierarchy and heterarchy in this steelwork plant, which will be reflected in a reduction of at least 99% of the time used for decision-making concerning the current system, which can lead to a decrease in refractory costs, energy consumption, and production cost. The effectiveness of the solution is demonstrated with scenario validation and expert evaluation.
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-03-04T13:39:49Z
dc.date.available.none.fl_str_mv	2022-03-04T13:39:49Z
dc.date.issued.none.fl_str_mv	2022
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.issn.spa.fl_str_mv	2088-8708
dc.identifier.uri.spa.fl_str_mv	https://hdl.handle.net/11323/9047
dc.identifier.doi.spa.fl_str_mv	10.11591/ijece.v12i3.pp2441-2453
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dc.identifier.instname.spa.fl_str_mv	Corporación Universidad de la Costa
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identifier_str_mv	2088-8708 10.11591/ijece.v12i3.pp2441-2453 2722-2578 Corporación Universidad de la Costa REDICUC - Repositorio CUC
url	https://hdl.handle.net/11323/9047 https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.ispartofjournal.spa.fl_str_mv	International Journal of Electrical and Computer Engineering (IJECE)
dc.relation.references.spa.fl_str_mv	[1] J. Madias, “Sistemas de control de procesos en la aceria,” Innovación, no. March, pp. 40–53, 2018. [2] A. A. Suarez Leon and J. R. Nunez Alvarez, “1D Convolutional Neural Network for Detecting Ventricular Heartbeats,” IEEE Latin America Transactions, vol. 17, no. 12, pp. 1970–1977, Dec. 2019, doi: 10.1109/TLA.2019.9011541. [3] J. Cancio, “Design and implementation of a supervision, control and monitoring system for the production of billets and corrugated bars through the use of SCADA software,” Universidad de Las Tunas, 2017. [4] R. A. Zambrano, “Procedure for making operational decisions programmed in the steel production process in the ACINOX Las Tunas company,” Universidad de Las Tunas, 2015. [5] Y. González Pérez and I. I. Kholod, “Use of multi-agent systems for machine learning,” Revista Ciencia e Ingeniería, vol. 41, no. 1, pp. 67–74, 2020. [6] M. Iglesias-Escudero, J. Villanueva-Balsera, F. Ortega-Fernandez, and V. Rodriguez-Montequín, “Planning and scheduling with uncertainty in the steel sector: A review,” Applied Sciences (Switzerland), vol. 9, no. 13, p. 2692, Jul. 2019, doi:10.3390/app9132692. [7] J. Backman, V. Kyllönen, and H. Helaakoski, “Methods and tools of improving steel manufacturing processes: Current state and future methods,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 1174–1179, 2019, doi: 10.1016/j.ifacol.2019.11.355. [8] J. Nuñez, I. Benítez, A. Rodríguez, S. Díaz, and D. de Oliveira, “Tools for the implementation of a SCADA system in a desalination process,” IEEE Latin America Transactions, vol. 17, no. 11, pp. 1858–1864, Nov. 2019, doi:10.1109/TLA.2019.8986424. [9] S. Kiyko, E. Druzhinin, O. Prokhorov, and B. Haidabrus, “Multi-agent model of energy consumption at the metallurgical enterprise,” in Lecture Notes in Mechanical Engineering, 2020, pp. 156–165. [10] R. Roy, B. A. Adesola, and S. Thornton, “Development of a knowledge model for managing schedule disturbance in steelmaking,” International Journal of Production Research, vol. 42, no. 18, pp. 3975–3994, Sep. 2004, doi:10.1080/00207540410001716453. [11] A. M. Riyad, M. S. Irfan Ahmed, and R. L. Raheemaa Khan, “An adaptive distributed intrusion detection system architecture using multi agents,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 6, pp. 4951–4960, Dec.2019, doi:10.11591/ijece.v9i6.pp4951-4960. [12] Y. Ozoe and M. Konishi, “Agent based scheduling of steel making processes,” in Proceedings of the 2009 IEEE International Conference on Networking, Sensing and Control, ICNSC 2009, Mar. 2009, pp. 278–281, doi: 10.1109/ICNSC.2009.4919286. [13] L. Wang, J. Zhao, W. Wang, and L. Cong, “Dynamic scheduling with production process reconfiguration for cold rolling line,” in IFAC Proceedings Volumes (IFAC-PapersOnline), Jan. 2011, vol. 44, no. 1 PART 1, pp. 12114–12119, doi: 10.3182/20110828-6-IT-1002.01296. [14] M. H. Fazel Zarandi and F. Kashani Azad, “A type 2 fuzzy multi agent based system for scheduling of steel production,” in Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, Jun. 2013, pp. 992–996, doi:10.1109/IFSA-NAFIPS.2013.6608535. [15] N. B. Gusareva, G. I. Andryushchenko, K. G. Tsaritova, V. V Zelenov, and L. N. Sorokina, “Energy enterprise risks analysis using fuzzy logic methods,” International Journal of Energy Economics and Policy, vol. 9, no. 3, pp. 366–372, May 2019, doi:10.32479/ijeep.7957. [16] J. R. Nũez-Alvarez, I. F. Benítez-Pina, and Y. Llosas-Albuerne, “Communications in flexible supervisor for laboratory research in renewable energy,” IOP Conference Series: Materials Science and Engineering, vol. 844, no. 1, Jun. 2020, doi: 10.1088/1757-899X/844/1/012016. [17] J. Du, P. Dong, V. Sugumaran, and D. Castro-Lacouture, “Dynamic decision support framework for production scheduling using a combined genetic algorithm and multiagent model,” Expert Systems, vol. 38, no. 1, Feb. 2021, doi: 10.1111/exsy.12533. [18] V. Iannino, M. Vannocci, M. Vannucci, V. Colla, and M. Neuer, “A multi-agent approach for the self-optimization of steel production,” International Journal of Simulation: Systems, Science and Technology, vol. 19, no. 5, pp. 20.1--20.7, Jan. 2018, doi:10.5013/IJSSST.a.19.05.20. [19] G. Santos, F. Silva, B. Teixeira, Z. Vale, and T. Pinto, “Power systems simulation using ontologies to enable the interoperability of multi-agent systems,” in 2018 Power Systems Computation Conference (PSCC), Jun. 2018, pp. 1–7, doi:10.23919/PSCC.2018.8442888. [20] D. Ryżko, Modern Big Data Architectures. Wiley, 2020. [21] G. Jezic, J. Chen-Burger, M. Kusek, R. Sperka, R. J. Howlett, and L. C. Jain, “Agents and multi-agent systems: technologies and applications,” 14th KES International Conference, KES-AMSTA 2020, 2020. [22] S. Jin, S. Wang, and F. Fang, “Game theoretical analysis on capacity configuration for microgrid based on multi-agent system, ”International Journal of Electrical Power and Energy Systems, vol. 125, Feb. 2021, doi: 10.1016/j.ijepes.2020.106485. [23] K. Patel and A. Mehta, “Discrete-time higher order sliding mode protocols for leader-following consensus of homogeneous discrete multi-agent system,” in Studies in Systems, Decision and Control, vol. 303, 2021, pp. 77–96. [24] A. Winnicka, K. Kęsik, D. Połap, M. Woźniak, and Z. Marszałek, “A multi-agent gamification system for managing smart homes,” Sensors (Switzerland), vol. 19, no. 5, Mar. 2019, doi: 10.3390/s19051249. [25] K. Moummadi, R. Abidar, H. Medromi, and A. Ziani, “Secured remote control of greenhouse based on wireless sensor network and multi agent systems,” in Advances in Intelligent Systems and Computing, vol. 912, 2019, pp. 427–439. [26] R. A. Nesterov, A. A. Mitsyuk, and I. A. Lomazova, “Simulating Behavior of Multi-Agent Systems with Acyclic Interactions of Agents,” Proceedings of the Institute for System Programming of the RAS, vol. 30, no. 3, pp. 285–302, 2018, doi:10.15514/ispras-2018-30(3)-20. [27] E. M. López, C. M. Godoy, L. G. M. Jimenéz, E. B. Guerrero, “Multi-agent support system for the analysis of a collaborative activity of a video game,” Pistas Educativas, vol. 39, no. 127, pp. 270–281, 2017. [28] Y. Demazeau, T. Holvoet, J. M. Corchado, and S. Costantini, Eds., Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection, vol. 12092. Cham: Springer International Publishing, 2020. [29] V. Colla, G. Nastasi, A. Maddaloni, N. Holzknecht, T. Heckenthaler, and G. Hartmann, “Intelligent control station for improved quality management in flat steel production,” IFAC-PapersOnLine, vol. 49, no. 20, pp. 226–231, 2016, doi:10.1016/j.ifacol.2016.10.125. [30] D. Ouelhadj, S. Petrovic, P. I. Cowling, and A. Meisels, “Inter-agent cooperation and communication for agent-based robust dynamic scheduling in steel production,” Advanced Engineering Informatics, vol. 18, no. 3, pp. 161–172, Jul. 2004, doi:10.1016/j.aei.2004.10.003. [31] P. I. Cowling, D. Ouelhadj, and S. Petrovic, “Dynamic scheduling of steel casting and milling using multi-agents,” Production Planning and Control, vol. 15, no. 2, pp. 178–188, Mar. 2004, doi: 10.1080/09537280410001662466. [32] L. L. Sun, H. Jin, H. Q. Jia, J. N. Hu, and Y. Li, “Research on steelmaking - Continuous casting production scheduling system based on virtual real fusion,” in 2017 IEEE International Conference on Information and Automation, ICIA 2017, Jul. 2017, pp. 1054–1059, doi: 10.1109/ICInfA.2017.8079058. [33] G. F. Angelo Martins and A. Batista De Almeida, “Automatic Power Restoration in Distribution Systems Modeled through Multiagent Systems,” IEEE Latin America Transactions, vol. 18, no. 10, pp. 1768–1776, Oct. 2020, doi:10.1109/TLA.2020.9387668. [34] J. Andramuo, E. Mendoza, J. Núez, and E. Liger, “Intelligent distributed module for local control of lighting and electrical outlets in a home,” Journal of Physics: Conference Series, vol. 1730, no. 1, Jan. 2021, doi: 10.1088/1742-6596/1730/1/012001. [35] J. R. Nũez et al., “Design of a fuzzy controller for a hybrid generation system,” IOP Conference Series: Materials Science and Engineering, vol. 844, no. 1, May 2020, doi: 10.1088/1757-899X/844/1/012017. [36] E. Mendoza, P. Fuentes, I. Benítez, D. Reina, and J. Núñez, “Network of multi-hop wireless sensors for low cost and extended area home automation systems,” RIAI - Revista Iberoamericana de Automatica e Informatica Industrial, vol. 17, no. 4, pp. 412–423, 2020, doi: 10.4995/RIAI.2020.12301.
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spelling	Ricardo Rodríguez, Angel RaúlBenítez, Israel FGonzález Yero, GuillermoNúñez Alvarez, José Ricardo2022-03-04T13:39:49Z2022-03-04T13:39:49Z20222088-8708https://hdl.handle.net/11323/904710.11591/ijece.v12i3.pp2441-24532722-2578Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This work was carried out in the company ACINOX Las Tunas, Cuba, to design an integrated automation architecture based on intelligent agents for control, monitoring, and decision-making in the production process that guarantees an improvement in planning and management of the process in the steelwork plant. The great differences of technologies and systems of each steel mill and the multiple restrictions, methods, and techniques, within a wide dynamic strongly concatenated, do not generalize automation systems feasibly. In our research, we use international research results and the experience of the plant technologists to create three levels of distributed intelligent architecture: business, production planning-control, and steel manufacturing. Each level manages to integrate and balance the particular and general interests for efficient decision-making combined between hierarchy and heterarchy in this steelwork plant, which will be reflected in a reduction of at least 99% of the time used for decision-making concerning the current system, which can lead to a decrease in refractory costs, energy consumption, and production cost. The effectiveness of the solution is demonstrated with scenario validation and expert evaluation.13 páginasapplication/pdfengInstitute of Advanced Engineering and Science (IAES)IndonesiaAtribución-CompartirIgual 4.0 Internacional (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Multi-agent system for steel manufacturing processArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionhttp://ijece.iaescore.com/index.php/IJECE/article/view/25786/15621International Journal of Electrical and Computer Engineering (IJECE)[1] J. Madias, “Sistemas de control de procesos en la aceria,” Innovación, no. March, pp. 40–53, 2018.[2] A. A. Suarez Leon and J. R. Nunez Alvarez, “1D Convolutional Neural Network for Detecting Ventricular Heartbeats,” IEEE Latin America Transactions, vol. 17, no. 12, pp. 1970–1977, Dec. 2019, doi: 10.1109/TLA.2019.9011541.[3] J. Cancio, “Design and implementation of a supervision, control and monitoring system for the production of billets and corrugated bars through the use of SCADA software,” Universidad de Las Tunas, 2017.[4] R. A. Zambrano, “Procedure for making operational decisions programmed in the steel production process in the ACINOX Las Tunas company,” Universidad de Las Tunas, 2015.[5] Y. González Pérez and I. I. Kholod, “Use of multi-agent systems for machine learning,” Revista Ciencia e Ingeniería, vol. 41, no. 1, pp. 67–74, 2020.[6] M. Iglesias-Escudero, J. Villanueva-Balsera, F. Ortega-Fernandez, and V. Rodriguez-Montequín, “Planning and scheduling with uncertainty in the steel sector: A review,” Applied Sciences (Switzerland), vol. 9, no. 13, p. 2692, Jul. 2019, doi:10.3390/app9132692.[7] J. Backman, V. Kyllönen, and H. Helaakoski, “Methods and tools of improving steel manufacturing processes: Current state and future methods,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 1174–1179, 2019, doi: 10.1016/j.ifacol.2019.11.355.[8] J. Nuñez, I. Benítez, A. Rodríguez, S. Díaz, and D. de Oliveira, “Tools for the implementation of a SCADA system in a desalination process,” IEEE Latin America Transactions, vol. 17, no. 11, pp. 1858–1864, Nov. 2019, doi:10.1109/TLA.2019.8986424.[9] S. Kiyko, E. Druzhinin, O. Prokhorov, and B. Haidabrus, “Multi-agent model of energy consumption at the metallurgical enterprise,” in Lecture Notes in Mechanical Engineering, 2020, pp. 156–165.[10] R. Roy, B. A. Adesola, and S. Thornton, “Development of a knowledge model for managing schedule disturbance in steelmaking,” International Journal of Production Research, vol. 42, no. 18, pp. 3975–3994, Sep. 2004, doi:10.1080/00207540410001716453.[11] A. M. Riyad, M. S. Irfan Ahmed, and R. L. Raheemaa Khan, “An adaptive distributed intrusion detection system architecture using multi agents,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 6, pp. 4951–4960, Dec.2019, doi:10.11591/ijece.v9i6.pp4951-4960.[12] Y. Ozoe and M. Konishi, “Agent based scheduling of steel making processes,” in Proceedings of the 2009 IEEE International Conference on Networking, Sensing and Control, ICNSC 2009, Mar. 2009, pp. 278–281, doi: 10.1109/ICNSC.2009.4919286.[13] L. Wang, J. Zhao, W. Wang, and L. Cong, “Dynamic scheduling with production process reconfiguration for cold rolling line,” in IFAC Proceedings Volumes (IFAC-PapersOnline), Jan. 2011, vol. 44, no. 1 PART 1, pp. 12114–12119, doi: 10.3182/20110828-6-IT-1002.01296.[14] M. H. Fazel Zarandi and F. Kashani Azad, “A type 2 fuzzy multi agent based system for scheduling of steel production,” in Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, Jun. 2013, pp. 992–996, doi:10.1109/IFSA-NAFIPS.2013.6608535.[15] N. B. Gusareva, G. I. Andryushchenko, K. G. Tsaritova, V. V Zelenov, and L. N. Sorokina, “Energy enterprise risks analysis using fuzzy logic methods,” International Journal of Energy Economics and Policy, vol. 9, no. 3, pp. 366–372, May 2019, doi:10.32479/ijeep.7957.[16] J. R. Nũez-Alvarez, I. F. Benítez-Pina, and Y. Llosas-Albuerne, “Communications in flexible supervisor for laboratory research in renewable energy,” IOP Conference Series: Materials Science and Engineering, vol. 844, no. 1, Jun. 2020, doi: 10.1088/1757-899X/844/1/012016.[17] J. Du, P. Dong, V. Sugumaran, and D. Castro-Lacouture, “Dynamic decision support framework for production scheduling using a combined genetic algorithm and multiagent model,” Expert Systems, vol. 38, no. 1, Feb. 2021, doi: 10.1111/exsy.12533.[18] V. Iannino, M. Vannocci, M. Vannucci, V. Colla, and M. Neuer, “A multi-agent approach for the self-optimization of steel production,” International Journal of Simulation: Systems, Science and Technology, vol. 19, no. 5, pp. 20.1--20.7, Jan. 2018, doi:10.5013/IJSSST.a.19.05.20.[19] G. Santos, F. Silva, B. Teixeira, Z. Vale, and T. Pinto, “Power systems simulation using ontologies to enable the interoperability of multi-agent systems,” in 2018 Power Systems Computation Conference (PSCC), Jun. 2018, pp. 1–7, doi:10.23919/PSCC.2018.8442888.[20] D. Ryżko, Modern Big Data Architectures. Wiley, 2020.[21] G. Jezic, J. Chen-Burger, M. Kusek, R. Sperka, R. J. Howlett, and L. C. Jain, “Agents and multi-agent systems: technologies and applications,” 14th KES International Conference, KES-AMSTA 2020, 2020.[22] S. Jin, S. Wang, and F. Fang, “Game theoretical analysis on capacity configuration for microgrid based on multi-agent system, ”International Journal of Electrical Power and Energy Systems, vol. 125, Feb. 2021, doi: 10.1016/j.ijepes.2020.106485.[23] K. Patel and A. Mehta, “Discrete-time higher order sliding mode protocols for leader-following consensus of homogeneous discrete multi-agent system,” in Studies in Systems, Decision and Control, vol. 303, 2021, pp. 77–96.[24] A. Winnicka, K. Kęsik, D. Połap, M. Woźniak, and Z. Marszałek, “A multi-agent gamification system for managing smart homes,” Sensors (Switzerland), vol. 19, no. 5, Mar. 2019, doi: 10.3390/s19051249.[25] K. Moummadi, R. Abidar, H. Medromi, and A. Ziani, “Secured remote control of greenhouse based on wireless sensor network and multi agent systems,” in Advances in Intelligent Systems and Computing, vol. 912, 2019, pp. 427–439.[26] R. A. Nesterov, A. A. Mitsyuk, and I. A. Lomazova, “Simulating Behavior of Multi-Agent Systems with Acyclic Interactions of Agents,” Proceedings of the Institute for System Programming of the RAS, vol. 30, no. 3, pp. 285–302, 2018, doi:10.15514/ispras-2018-30(3)-20.[27] E. M. López, C. M. Godoy, L. G. M. Jimenéz, E. B. Guerrero, “Multi-agent support system for the analysis of a collaborative activity of a video game,” Pistas Educativas, vol. 39, no. 127, pp. 270–281, 2017.[28] Y. Demazeau, T. Holvoet, J. M. Corchado, and S. Costantini, Eds., Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection, vol. 12092. Cham: Springer International Publishing, 2020.[29] V. Colla, G. Nastasi, A. Maddaloni, N. Holzknecht, T. Heckenthaler, and G. Hartmann, “Intelligent control station for improved quality management in flat steel production,” IFAC-PapersOnLine, vol. 49, no. 20, pp. 226–231, 2016, doi:10.1016/j.ifacol.2016.10.125.[30] D. Ouelhadj, S. Petrovic, P. I. Cowling, and A. Meisels, “Inter-agent cooperation and communication for agent-based robust dynamic scheduling in steel production,” Advanced Engineering Informatics, vol. 18, no. 3, pp. 161–172, Jul. 2004, doi:10.1016/j.aei.2004.10.003.[31] P. I. Cowling, D. Ouelhadj, and S. Petrovic, “Dynamic scheduling of steel casting and milling using multi-agents,” Production Planning and Control, vol. 15, no. 2, pp. 178–188, Mar. 2004, doi: 10.1080/09537280410001662466.[32] L. L. Sun, H. Jin, H. Q. Jia, J. N. Hu, and Y. Li, “Research on steelmaking - Continuous casting production scheduling system based on virtual real fusion,” in 2017 IEEE International Conference on Information and Automation, ICIA 2017, Jul. 2017, pp. 1054–1059, doi: 10.1109/ICInfA.2017.8079058.[33] G. F. 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Núñez, “Network of multi-hop wireless sensors for low cost and extended area home automation systems,” RIAI - Revista Iberoamericana de Automatica e Informatica Industrial, vol. 17, no. 4, pp. 412–423, 2020, doi: 10.4995/RIAI.2020.12301.24532441312AgentsArtificial intelligenceDecision support systemsIntegrated manufacturingIntelligent controlPublicationORIGINALMulti-agent system for steel manufacturing process.pdfMulti-agent system for steel manufacturing process.pdfapplication/pdf682604https://repositorio.cuc.edu.co/bitstreams/3a79916e-5207-484b-aa8a-60f0bd2bbd84/download189e41d3e3af3ac235c830fc9a4666c7MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/54fbe501-c371-403d-9800-ade8f517b962/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTMulti-agent system for steel manufacturing process.pdf.txtMulti-agent system for steel manufacturing 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Multi-agent system for steel manufacturing process

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