A Hybrid Artificial Intelligence-Based System for Supporting Eco-Industrialization of Complex Manufacturing Processes

Abstract:

Since the industrialization phase is one of the main phases of the product development cycle, an original hybrid flexible automated system is developed in this paper in order to support production eco-processes designers’ decision making. It is based on three different artificial intelligence tools, namely the fuzzy Ontologies, the cases based reasoning and the rules based reasoning, which have been integrated in one system. Actually, the proposed system is composed of different modules that are well described in details thereafter. In the end of this paper, a case of study is presented in order to illustrate the efficacy of the developed intelligent system.

• Periodical: International Journal of Engineering Research in Africa (Volume 64)
• Pages: 147-171
• DOI: https://doi.org/10.4028/p-2vxi5u
• Online since: June 2023
• Authors: Chaimae Abadi*, Imad Manssouri, Mohammed Abadi, Asmae Abadi, Hassane Sahbi
• Keywords: Artificial Intelligence, Automated Decision Making System, CBR & RBR, Fuzzy Ontologies, Industrialization, Production Eco-Processes Design, Production Line

References

[1] F. Baader, D. Calvanese, D. McGuinness, P. Patel-Schneider, D. Nardi, The Description Logic Handbook: Theory, Implementation and Applications, in: Cambridge University press, 2003.

DOI: 10.1017/cbo9780511711787

[2] R. Calegari, G. Ciatto, V. Mascardi, A. Omicini, Logic-based technologies for multi-agent systems: A systematic literature review, Autonomous Agents and Multi-Agent Systems, (2021), 35(1), 1-67.

DOI: 10.1007/s10458-020-09478-3

[3] S. Calegari, D. Ciucci, Fuzzy ontology, fuzzy description logics and fuzzy-owl, in: International Workshop on Fuzzy Logic and Applications, Berlin, Heidelberg , Springer, (2007), p.118–126.

DOI: 10.1007/978-3-540-73400-0_15

[4] E. M. H. Saeed, Article Review: Survey Fuzzy Logic and Aprior Algorithms Employed for E-learning Environment. Turkish Journal of Computer and Mathematics Education (TURCOMAT), (2021), 12(8), pp.60-69.

[5] C. Carlsson, M. Brunelli, J. Mezei. Decision making with a fuzzy ontology, in: Soft. Comput. (16), 2012, p.1143–1152.

DOI: 10.1007/s00500-011-0789-x

[6] B. Díaz-Agudo & P.A. González-Calero. An architecture for knowledge intensive CBR systems, in: European workshop on advances in case-based reasoning, Berlin, Heidelberg, 2000, pp.37-48. Springer.

DOI: 10.1007/3-540-44527-7_5

[7] A. Martin, S. Emmenegger, K. Hinkelmann & B. Thönssen. A viewpoint-based case-based reasoning approach utilising an enterprise architecture ontology for experience management, in: Enterprise Information Systems. 11(4), 2017, pp.551-575.

DOI: 10.1080/17517575.2016.1161239

[8] TP. Lim, W. Husain and N. Zakaria. Recommender System for Personalised Wellness Therapy, International Journal of Advanced Computer Science and Applications. 4 (2013) 54-60.

DOI: 10.14569/ijacsa.2013.040909

[9] P.K. Singh & P. Sarkar. Eco-design approaches for developing eco-friendly products: a review, Advances in Industrial and Production Engineering, (2019) 185-192.

DOI: 10.1007/978-981-13-6412-9_17

[10] Z. Xu, Z. Lv, J. Li, H. Sun & Z. Sheng. A Novel Perspective on Travel Demand Prediction Considering Natural Environmental and Socioeconomic Factors, in:  IEEE Intelligent Transportation Systems Magazine, 2022.

DOI: 10.1109/mits.2022.3162901

[11] R. Vinuesa et al. The role of artificial intelligence in achieving the Sustainable Development Goals, in: Nature communications, 11(1), 2020, pp.1-10.

[12] Z. Xu, J. Li, Z. Lv, C. Dong & L. Fu. A classification method for urban functional regions based on the transfer rate of empty cars, in: IET Intelligent Transport Systems, 16(2), 2022, pp.133-147.

DOI: 10.1049/itr2.12134

[13] E.R. Platcheck, L. Schaeffer, W. Jr. Kindlein and L. Candido. Methodology of ecodesign for the development of more sustainable electro-electronic equipment. J. Clean. Prod. 16, 2008, p.75–86.

DOI: 10.1016/j.jclepro.2006.10.006

[14] G. Taddese, S. Durieux & E. Duc. Sustainability performance indicators for additive manufacturing: a literature review based on product life cycle studies, The International Journal of Advanced Manufacturing Technology.107(7) (2020) 3109-3134.

DOI: 10.1007/s00170-020-05249-2

[15] IHOBE. Manual práctico de ecodiseño: operativa de implantación en siete pasos,2000.

[16] L.M. Agudelo Gutierrez. Aide à décision en conception préliminaire par l'estimation du poids de la performance environnementale (Doctoral dissertation, Paris, ENSAM), 2016.

[17] P. Paulraj et al. Environmentally conscious manufacturing and life cycle analysis: a state-of-the-art survey, Journal of Nanomaterials (2022).

[18] C. Y. Ng & W. C Tang. Evaluation of design options for green product development: a combined Cuckoo search and life cycle assessment approach, The International Journal of Life Cycle Assessment 27(5) (2022) 665-679.

DOI: 10.1007/s11367-022-02056-7

[19] A. Ghoroghi, Y. Rezgui, I. Petri & T. Beach. Advances in application of machine learning to life cycle assessment: a literature review, The International Journal of Life Cycle Assessment (2022) 1-24.

DOI: 10.1007/s11367-022-02030-3

[20] M. Germani, M. Dufrene, M. Mandolini, M. Marconi, P. Zwolinski. Integrated software platform for green engineering design and product sustainability, in: Re-engineeringManufacturing for Sustainability (2013, p.87–92. Springer, Singapore.

DOI: 10.1007/978-981-4451-48-2_14

[21] M.A. Cusenza, S. Bobba, F. Ardente, M.Cellura & F. Di Persio. Energy and environmental assessment of a traction lithium-ion battery pack for plug-in hybrid electric vehicles, in: Journal of cleaner production, 215, 2019, pp.634-649.

DOI: 10.1016/j.jclepro.2019.01.056

[22] D. Böckin & A. M Tillman. Environmental assessment of additive manufacturing in the automotive industry, Journal of Cleaner Production  226 (2019) 977-987.

DOI: 10.1016/j.jclepro.2019.04.086

[23] X. Zhang, M. Zhang, H. Zhang, Z. Jiang, C. Liu & W. Cai. A review on energy, environment and economic assessment in remanufacturing based on life cycle assessment method, Journal of Cleaner Production (2020) 255-120160.

DOI: 10.1016/j.jclepro.2020.120160

[24] C. Abadi, I. Manssouri & A. Abadi. A Fuzzy Ontology Based Approach to Support Product Eco-Design, in:  International Conference on Artificial Intelligence & Industrial Applications, Springer, Cham, 2020, pp.1-13.

DOI: 10.1007/978-3-030-51186-9_1

[25] E. Sdrolia & G. Zarotiadis. A comprehensive review for green product term: From definition to evaluation, Journal of Economic Surveys 33(1) (2019) 150-178.

DOI: 10.1111/joes.12268

[26] D. Jugend, M. A. P. Pinheiro, J. V. R Luiz, A. V. Junior & P. ACauchick-Miguel. Achieving environmental sustainability with ecodesign practices and tools for new product development. In Innovation strategies in environmental science, Elsevier, 2020, pp.179-207.

DOI: 10.1016/b978-0-12-817382-4.00006-x

[27] S. K Takalo & H. S Tooranloo. Green innovation: A systematic literature review, Journal of Cleaner Production 279 (2021) 122474.

DOI: 10.1016/j.jclepro.2020.122474

[28] F. Bobillo, U. Straccia. The fuzzy ontology reasoner fuzzyDL, in: Knowl.-Based Syst. (95), 2016, p.12–34.

DOI: 10.1016/j.knosys.2015.11.017

[29] C. Abadi, I. Manssouri, , et A. Abadi. An Artificial-Intelligent-Based System to Automate the Design of Complex Mechanical Products, International Journal of Engineering Research in Africa, Trans Tech Publications Ltd. (2022) 247-274.

DOI: 10.4028/www.scientific.net/jera.58.247

[30] A. Abadi, H. Ben-Azza, S. Sekkat, E.M. Zemmouri. A Fuzzy Ontology-based approach for multi-criteria decision-making: a use case in supplier selection, in: Congrès International Du Génie Industriel Et Du Management Des Systèmes, Meknes, 2017.