Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes
Shaymaa Hussein Nowfal
Shaymaa Hussein Nowfal
Department of Medical Physics, College of Sciences, University of Warith Al-Anbiyaa Karbala, Iraq and Department of Medical Physics, College of Applied Medical Sciences, University of Kerbala, Karbala, Iraq.
Sustainable Manufacturing Practices (SMP), particularly in the selection of materials, have become essential due to environmental issues caused by the expansion of industry. Compared to conventional polymers, biodegradable Polymer Materials (BPM) are growing more commonly as an approach to reducing trash pollution. Suitable materials can be challenging due to numerous considerations, like ecological impact, expenditure, and material properties. When addressing sophisticated trade-offs, standard approaches drop. To compete with such challenges, employing Genetic Algorithms (GA) may be more successful, as they have their foundation in the basic concepts of biological development and the natural selection process. With a focus on BPM, this study provides a GA model for optimal packaging substance selection. Out of the four algorithms for computation used for practical testing—PSO, ACO, and SA—the GA model is the most effective. The findings demonstrate that GA can be used to enhance SMP and performs well in enormous search spaces that contain numerous different combinations of materials.
M. Abubakr, A. T. Abbas, I. Tomaz, M. S. Soliman, M. Luqman, and H. Hegab, “Sustainable and Smart Manufacturing: An Integrated Approach,” Sustainability, vol. 12, no. 6, p. 2280, Mar. 2020, doi: 10.3390/su12062280.
C. Enyoghasi and F. Badurdeen, “Industry 4.0 for sustainable manufacturing: Opportunities at the product, process, and system levels,” Resources, Conservation and Recycling, vol. 166, p. 105362, Mar. 2021, doi: 10.1016/j.resconrec.2020.105362.
C. Maraveas, “Production of Sustainable and Biodegradable Polymers from Agricultural Waste,” Polymers, vol. 12, no. 5, p. 1127, May 2020, doi: 10.3390/polym12051127.
S. Gopalakrishnan, N. W. Hartman, and M. D. Sangid, “Model-Based Feature Information Network (MFIN): A Digital Twin Framework to Integrate Location-Specific Material Behavior Within Component Design, Manufacturing, and Performance Analysis,” Integrating Materials and Manufacturing Innovation, vol. 9, no. 4, pp. 394–409, Nov. 2020, doi: 10.1007/s40192-020-00190-4.
E. S. Hosseini, S. Dervin, P. Ganguly, and R. Dahiya, “Biodegradable Materials for Sustainable Health Monitoring Devices,” ACS Applied Bio Materials, vol. 4, no. 1, pp. 163–194, Dec. 2020, doi: 10.1021/acsabm.0c01139.
B. Alhijawi and A. Awajan, “Genetic algorithms: theory, genetic operators, solutions, and applications,” Evolutionary Intelligence, vol. 17, no. 3, pp. 1245–1256, Feb. 2023, doi: 10.1007/s12065-023-00822-6.
Y. Zhang, D. W. Apley, and W. Chen, “Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables,” Scientific Reports, vol. 10, no. 1, Mar. 2020, doi: 10.1038/s41598-020-60652-9.
C. Kim, R. Batra, L. Chen, H. Tran, and R. Ramprasad, “Polymer design using genetic algorithm and machine learning,” Computational Materials Science, vol. 186, p. 110067, Jan. 2021, doi: 10.1016/j.commatsci.2020.110067.
J. Cai, X. Chu, K. Xu, H. Li, and J. Wei, “Machine learning-driven new material discovery,” Nanoscale Advances, vol. 2, no. 8, pp. 3115–3130, 2020, doi: 10.1039/d0na00388c.
S. M. Moosavi, K. M. Jablonka, and B. Smit, “The Role of Machine Learning in the Understanding and Design of Materials,” Journal of the American Chemical Society, vol. 142, no. 48, pp. 20273–20287, Nov. 2020, doi: 10.1021/jacs.0c09105.
S. Talatahari, M. Azizi, and A. H. Gandomi, “Material Generation Algorithm: A Novel Metaheuristic Algorithm for Optimization of Engineering Problems,” Processes, vol. 9, no. 5, p. 859, May 2021, doi: 10.3390/pr9050859.
S. O. Ongbali, S. A. Afolalu, E. Y. Salawu and S. A. Omotehinse, "Review of Project Monitoring and Evaluation Technique as Index for Sustainable Manufacturing Performance," 2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), Omu-Aran, Nigeria, 2023, pp. 1-5, doi: 10.1109/SEB-SDG57117.2023.10124516.
N. Ganjavi and H. Fazlollahtabar, "Integrated Sustainable Production Value Measurement Model Based on Lean and Six Sigma in Industry 4.0 Context," in IEEE Transactions on Engineering Management, vol. 70, no. 6, pp. 2320-2333, June 2023, doi: 10.1109/TEM.2021.3078169.
M. S. K. Pheng and L. G. David, "Artificial Intelligence in Back-End Semiconductor Manufacturing: A Case Study," 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), Ballari, India, 2022, pp. 1-4, doi: 10.1109/ICDCECE53908.2022.9792976.
N. M. Durakbasa, G. Bas, D. Riepl, and J. M. Bauer, “An innovative educational concept of teleworking in the high precision metrology laboratory to develop a model of implementation in the advanced manufacturing industry,” Proceedings of 2015 12th International Conference on Remote Engineering and Virtual Instrumentation (REV), Feb. 2015, doi: 10.1109/rev.2015.7087288.
C. H. Yang, “Development of intelligent building management system evaluation and selection for smart factory: An integrated MCDM approach,” 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Dec. 2017, doi: 10.1109/ieem.2017.8290144.
B. B. Kanbur, S. Shen, Y. Zhou and F. Duan, "Neural network-integrated multiobjective optimization of the 3D-printed conformal cooling channels," 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia, 2020, pp. 1-6, doi: 10.23919/SpliTech49282.2020.9243730.
R. Poler et al., "An IoT-based Reliable Industrial Data Services for Manufacturing Quality Control," 2021 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Cardiff, United Kingdom, 2021, pp. 1-8, doi: 10.1109/ ICE/ITMC52061.2021. 9570203.
Acknowledgements
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Sudhakar Sengan
Sudhakar Sengan
Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Shaymaa Hussein Nowfal, Vijaya Bhaskar Sadu, Sudhakar Sengan, Rajeshkumar G, Anjaneyulu Naik R and Sreekanth K, “Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes”, Journal of Machine and Computing, pp. 563-574, July 2024. doi: 10.53759/7669/jmc202404054.
