The Use of Simulation and Artificial Intelligence as a Decision Support Tool for Sustainable Production Lines

Abstract:

In recent years, the general population has become increasingly aware of the importance of adopting more sustainable lifestyles. For companies, the implementation of sustainable systems is essential. This study aims to examine the contribution of simulation in combination with artificial intelligence (AI) to the sustainability of production lines. Simulation plays a crucial role for managers, as it allows them to predict future scenarios based on past experiences, allowing for more informed with the rise of digitization in the industry, it is now possible to manage resources such as energy and water in a more efficient manner. This is achieved through the use of techniques such as data scanning, communication with intelligent industrial sensors, known as the Industrial Internet of Things (IIoT), and the application of optimization and AI-based solutions to tackle complex problems, both in terms of efficiency and sustainability. This analysis has confirmed the significance of simulation when partnered with AI in improving the sustainability of production lines. This is because they offer the means to improve resource management from an economic, environmental, and social perspective.

• Periodical: Advances in Science and Technology (Volume 132)
• Pages: 405-412
• DOI: https://doi.org/10.4028/p-Cv6rt1
• Online since: October 2023
• Authors: Monica G. Cardoso*, Enrique Ares, Luis Pinto Ferreira, Gustavo Peláez
• Keywords: Digital Twin, Manufacturing Systems, Simulation, Sustainability

References

[1] Z. Kang, C. Catal, and B. Tekinerdogan, "Machine learning applications in production lines: A systematic literature review," Computers & Industrial Engineering, vol. 149, p.106773, 2020.

DOI: 10.1016/j.cie.2020.106773

[2] H. Guo, M. Chen, K. Mohamed, T. Qu, S. Wang, and J. Li, "A digital twin-based flexible cellular manufacturing for optimization of air conditioner line," Journal of Manufacturing Systems, vol. 58, pp.65-78, 2021.

DOI: 10.1016/j.jmsy.2020.07.012

[3] Z. Zhou, Y. Dou, J. Sun, J. Jiang, and Y. Tan, "Sustainable production line evaluation based on evidential reasoning," Sustainability, vol. 9, no. 10, p.1811, 2017.

DOI: 10.3390/su9101811

[4] R. El-Khalil and Z. Darwish, "Flexible manufacturing systems performance in US automotive manufacturing plants: a case study," Production planning & control, vol. 30, no. 1, pp.48-59, 2019.

DOI: 10.1080/09537287.2018.1520318

[5] N. Tuptuk and S. Hailes, "Security of smart manufacturing systems," Journal of Manufacturing Systems, vol. 47, pp.93-106, 2018/04/01/ 2018.

DOI: 10.1016/j.jmsy.2018.04.007

[6] C. Rosa, F. Silva, and L. P. Ferreira, "Improving the quality and productivity of steel wire-rope assembly lines for the automotive industry," Procedia Manufacturing, vol. 11, pp.1035-1042, 2017.

DOI: 10.1016/j.promfg.2017.07.214

[7] M. Balaji, S. Dinesh, S. Raja, R. Subbiah, and P. M. Kumar, "Lead time reduction and process enhancement for a low volume product," Materials Today: Proceedings, vol. 62, pp.1722-1728, 2022.

DOI: 10.1016/j.matpr.2021.12.240

[8] E. Oztemel and S. Gursev, "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, vol. 31, no. 1, pp.127-182, 2020.

DOI: 10.1007/s10845-018-1433-8

[9] K. A. Kurniadi and K. Ryu, "Maintaining sustainability in reconfigurable manufacturing systems featuring green-BOM," International Journal of Precision Engineering and Manufacturing-Green Technology, vol. 7, no. 3, pp.755-767, 2020.

DOI: 10.1007/s40684-020-00215-5

[10] A. Moldavska and T. Welo, "The concept of sustainable manufacturing and its definitions: A content-analysis based literature review," Journal of Cleaner Production, vol. 166, pp.744-755, 2017/11/10/ 2017.

DOI: 10.1016/j.jclepro.2017.08.006

[11] G. Uva, M. Dassisti, F. Iannone, G. Florio, F. Maddalena, M. Ruta, A. Grieco, I. Giannoccaro, V. Albino, M. Lezoche, A. Aubry, A. Giovannini, A. Buscicchio, Y. Eslami, and V. Leggieri, "Modelling Framework for Sustainable Co-management of Multi-purpose Exhibition Systems: The "Fiera del Levante" Case," Procedia Engineering, vol. 180, pp.812-821, 2017/01/01/ 2017.

DOI: 10.1016/j.proeng.2017.04.242

[12] M. Naderi, G. Peláez, E. Ares, and A. Fernández, "Sustainability Assessment Methodology (SAM) to improve decision-making in manufacturing companies," Procedia Manufacturing, vol. 41, pp.960-967, 2019.

DOI: 10.1016/j.promfg.2019.10.021

[13] F. J. Gomes Silva, K. Kirytopoulos, L. Pinto Ferreira, J. C. Sá, G. Santos, and M. C. Cancela Nogueira, "The three pillars of sustainability and agile project management: How do they influence each other," Corporate Social Responsibility and Environmental Management, 2022.

DOI: 10.1002/csr.2287

[14] K. Shibin, A. Gunasekaran, and R. Dubey, "Flexible sustainable manufacturing via decision support simulation: a case study approach," Sustainable Production and Consumption, vol. 12, pp.206-220, 2017.

DOI: 10.1016/j.spc.2017.08.001

[15] Y.-C. Lin, C.-C. Yeh, W.-H. Chen, W.-C. Liu, and J.-J. Wang, "The use of big data for sustainable development in motor production line issues," Sustainability, vol. 12, no. 13, p.5323, 2020.

DOI: 10.3390/su12135323

[16] M. Fathi, A. Nourmohammadi, M. Ghobakhloo, and M. Yousefi, "Production sustainability via supermarket location optimization in assembly lines," Sustainability, vol. 12, no. 11, p.4728, 2020.

DOI: 10.3390/su12114728

[17] M. C. Jena, S. K. Mishra, and H. S. Moharana, "Application of Industry 4.0 to enhance sustainable manufacturing," Environmental Progress & Sustainable Energy, vol. 39, no. 1, p.13360, 2020.

DOI: 10.1002/ep.13360

[18] A. Realyvásquez-Vargas, K. C. Arredondo-Soto, J. Blanco-Fernandez, J. D. Sandoval-Quintanilla, E. Jiménez-Macías, and J. L. García-Alcaraz, "Work standardization and anthropometric workstation design as an integrated approach to sustainable workplaces in the manufacturing industry," Sustainability, vol. 12, no. 9, p.3728, 2020.

DOI: 10.3390/su12093728

[19] A. D. Ioana, E. D. Maria, and V. Cristina, "Case study regarding the implementation of one-piece flow line in automotive company," Procedia Manufacturing, vol. 46, pp.244-248, 2020.

DOI: 10.1016/j.promfg.2020.03.036

[20] R. Guerrero, A. Serrano-Hernandez, J. Pascual, and J. Faulin, "Simulation Model for Wire Harness Design in the Car Production Line Optimization Using the SimPy Library," Sustainability, vol. 14, no. 12, p.7212, 2022.

DOI: 10.3390/su14127212

[21] L. Yavuz, A. Önen, S. Muyeen, and I. Kamwa, "Transformation of microgrid to virtual power plant–a comprehensive review," IET generation, transmission & distribution, vol. 13, no. 11, pp.1994-2005, 2019.

DOI: 10.1049/iet-gtd.2018.5649

[22] L. P. Ferreira, E. A. Gómez, G. C. P. Lourido, J. D. Quintas, and B. Tjahjono, "Analysis and optimisation of a network of closed-loop automobile assembly line using simulation," The International Journal of Advanced Manufacturing Technology, vol. 59, no. 1, pp.351-366, 2012.

DOI: 10.1007/s00170-011-3502-4

[23] E. Ares, G. Pelaez, L. P. Ferreira, M. D. Prieto, and M. A. Chao, "Optimisation of a production line using simulation and lean techniques," in Proceedings of the Operational Research Society Simulation Workshop (SW12). https://www. researchgate. net/publication/289858280, 2012.

[24] L. P. Ferreira, E. Ares, G. Peláez, A. Resano, C. Luis-Pérez, and B. Tjahjono, "Simulation of a closed-loops assembly line," in Key Engineering Materials, 2012, vol. 502: Trans Tech Publ, pp.127-132.

DOI: 10.4028/www.scientific.net/kem.502.127

[25] M. M. Aldurgam, M. Y. Alghadeer, M. A. Abdel-Aal, and S. Z. Selim, "Productivity Improvement Through Multi-Objective Simulation Optimization—A Case Study," IEEE Access, vol. 7, pp.40230-40239, 2019.

DOI: 10.1109/access.2019.2907403

[26] D. Piromalis and A. Kantaros, "Digital Twins in the Automotive Industry: The Road toward Physical-Digital Convergence," Applied System Innovation, vol. 5, no. 4, p.65, 2022.

DOI: 10.3390/asi5040065

[27] M. Liu, S. Fang, H. Dong, and C. Xu, "Review of digital twin about concepts, technologies, and industrial applications," Journal of Manufacturing Systems, vol. 58, pp.346-361, 2021.

DOI: 10.1016/j.jmsy.2020.06.017

[28] M. Ghita, B. Siham, M. Hicham, and H. Griguer, "Digital Twins Based LCA and ISO 20140 for Smart and Sustainable Manufacturing Systems," in Sustainable Intelligent Systems: Springer, 2021, pp.101-145.

DOI: 10.1007/978-981-33-4901-8_8

[29] Z. Zhang, F. Wen, Z. Sun, X. Guo, T. He, and C. Lee, "Artificial Intelligence‐Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin," Advanced Intelligent Systems, p.2100228, 2022.

DOI: 10.1002/aisy.202100228

[30] A. K. Sleiti, J. S. Kapat, and L. Vesely, "Digital twin in energy industry: Proposed robust digital twin for power plant and other complex capital-intensive large engineering systems," Energy Reports, vol. 8, pp.3704-3726, 2022.

DOI: 10.1016/j.egyr.2022.02.305

[31] J.-P. Schöggl, L. Stumpf, and R. J. Baumgartner, "The narrative of sustainability and circular economy-A longitudinal review of two decades of research," Resources, Conservation and Recycling, vol. 163, p.105073, 2020.

DOI: 10.1016/j.resconrec.2020.105073

[32] G. A. Gericke, R. B. Kuriakose, H. J. Vermaak, and O. Mardsen, "Design of digital twins for optimization of a water bottling plant," in IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society, 2019, vol. 1: IEEE, pp.5204-5210.

DOI: 10.1109/iecon.2019.8926880

[33] A. F. Mendi, "A Digital Twin Case Study on Automotive Production Line," Sensors, vol. 22, no. 18, p.6963, 2022.

DOI: 10.3390/s22186963

[34] J. F. Arinez, Q. Chang, R. X. Gao, C. Xu, and J. Zhang, "Artificial intelligence in advanced manufacturing: Current status and future outlook," Journal of Manufacturing Science and Engineering, vol. 142, no. 11, 2020.

DOI: 10.1115/1.4047855

[35] R. S. Peres, X. Jia, J. Lee, K. Sun, A. W. Colombo, and J. Barata, "Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook," IEEE Access, vol. 8, pp.220121-220139, 2020.

DOI: 10.1109/access.2020.3042874

[36] J. Lee, H. Davari, J. Singh, and V. Pandhare, "Industrial Artificial Intelligence for industry 4.0-based manufacturing systems," Manufacturing Letters, vol. 18, pp.20-23, 2018/10/01/ 2018.

DOI: 10.1016/j.mfglet.2018.09.002

[37] V. Kharchenko, O. Illiashenko, O. Morozova, and S. Sokolov, "Combination of digital twin and artificial intelligence in manufacturing using industrial IoT," in 2020 IEEE 11th international conference on dependable systems, services and technologies (DESSERT), 2020: IEEE, pp.196-201.

DOI: 10.1109/dessert50317.2020.9125038

[38] V. Pandiyan, S. Shevchik, K. Wasmer, S. Castagne, and T. Tjahjowidodo, "Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: A review," Journal of Manufacturing Processes, vol. 57, pp.114-135, 2020/09/01/ 2020.

DOI: 10.1016/j.jmapro.2020.06.013

[39] D. Rolnick, P. L. Donti, L. H. Kaack, K. Kochanski, A. Lacoste, K. Sankaran, A. S. Ross, N. Milojevic-Dupont, N. Jaques, and A. Waldman-Brown, "Tackling climate change with machine learning," ACM Computing Surveys (CSUR), vol. 55, no. 2, pp.1-96, 2022.

DOI: 10.1145/3485128

[40] C.-F. Chien, S. Dauzère-Pérès, W. T. Huh, Y. J. Jang, and J. R. Morrison, "Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies," vol. 58, ed: Taylor & Francis, 2020, pp.2730-2731.

DOI: 10.1080/00207543.2020.1752488

[41] A. Manimuthu, V. Venkatesh, Y. Shi, V. R. Sreedharan, and S. L. Koh, "Design and development of automobile assembly model using federated artificial intelligence with smart contract," International Journal of Production Research, vol. 60, no. 1, pp.111-135, 2022.

DOI: 10.1080/00207543.2021.1988750

[42] A. Manimuthu, V. Venkatesh, V. Raja Sreedharan, and V. Mani, "Modelling and analysis of artificial intelligence for commercial vehicle assembly process in VUCA world: a case study," International Journal of Production Research, vol. 60, no. 14, pp.4529-4547, 2022.

DOI: 10.1080/00207543.2021.1910361

[43] W. Lihao and D. Yanni, "A fault diagnosis method of tread production line based on convolutional neural network," in 2018 IEEE 9th international conference on software engineering and service science (ICSESS), 2018: IEEE, pp.987-990.

DOI: 10.1109/icsess.2018.8663824

[44] C. Li, H. Wang, and B. Li, "Performance prediction of a production line with variability based on grey model artificial neural network," in 2016 35th Chinese Control Conference (CCC), 2016: IEEE, pp.9582-9587.

DOI: 10.1109/chicc.2016.7554879

[45] A. Jamwal, R. Agrawal, and M. Sharma, "Deep learning for manufacturing sustainability: Models, applications in Industry 4.0 and implications," International Journal of Information Management Data Insights, vol. 2, no. 2, p.100107, 2022.

DOI: 10.1016/j.jjimei.2022.100107