Issue
Mechanics & Industry
Volume 26, 2025
Artificial Intelligence in Mechanical Manufacturing: From Machine Learning to Generative Pre-trained Transformer
Article Number 34
Number of page(s) 13
DOI https://doi.org/10.1051/meca/2025025
Published online 28 November 2025
  1. Q. Tian, S. Zhang, H. Yu, Exploring the factors influencing business model innovation using grounded theory: the case of a Chinese high-end equipment manufacturer, Sustainability, 11, 1455 (2019) [Google Scholar]
  2. A. Trabelsi, M.A. Rezgui, M. Amdouni, Robust design optimization of dynamic and static manufacturing processes using the stochastic frontier model, Mechanics & Industry, 26, 1 (2025) [Google Scholar]
  3. C. Chivu, M. Afteni, G.R. Frumusanu, Method for holistic optimization of the manufacturing process numerically described as low-dimensional database, Mechanics & Industry, 25, 17 (2024) [Google Scholar]
  4. Y. Li, Y. Wu, Y. Chen, The influence of foreign direct investment and trade opening on green total factor productivity in the equipment manufacturing industry, Appl. Econ. 53, 6641–6654 (2021) [Google Scholar]
  5. Y. Song, L. Yang, S. Sindakis, Analyzing the role of high-tech industrial agglomeration in green transformation and upgrading of manufacturing industry: the case of China, J. Knowl. Econ. 14, 3847–3877 (2023) [Google Scholar]
  6. Y. Guo, W. Zhang, Q. Qin, Intelligent manufacturing management system based on data mining in artificial intelligence energy-saving resources, Soft Comput. 27, 4061–4076 (2023) [Google Scholar]
  7. B. Naghshineh, H. Carvalho, Exploring the interrelations between additive manufacturing adoption barriers and supply chain vulnerabilities: the case of an original equipment manufacturer, J. Manuf. Technol. Manag. 33, 1473–1489 (2022) [Google Scholar]
  8. Z. Dou, Y. Sun, Y. Zhang, Regional manufacturing industry demand forecasting: a deep learning approach, Appl. Sci. 11, 6199 (2021) [Google Scholar]
  9. N. Manjunatha, Internationalization and innovation capabilities determine export performance of Indian auto component manufacturing industry, Pacific Bus. Rev. Int. 13, 105–116 (2021) [Google Scholar]
  10. S. Min, Z.G. Zacharia, C.D. Smith, Defining supply chain management: in the past, present, and future, J. Bus. Logist. 40, 44–55 (2019) [CrossRef] [MathSciNet] [Google Scholar]
  11. K. Zekhnini, A. Cherrafi, I. Bouhaddou, Supply chain management 4.0: a literature review and research framework, Benchmark.: Int. J. 28, 465–501 (2021) [Google Scholar]
  12. M. Ben-Daya, E. Hassini, Z. Bahroun, Internet of things and supply chain management: a literature review, Int. J. Prod. Res. 57, 4719–4742 (2019) [Google Scholar]
  13. A. Wieland, Dancing the supply chain: toward transformative supply chain management, J. Supply Chain Manag. 57, 58–73 (2021) [Google Scholar]
  14. P. Helo, Y. Hao, Artificial intelligence in operations management and supply chain management: an exploratory case study, Prod. Plan. Control, 33, 1573–1590 (2022) [Google Scholar]
  15. M.M.S. Sodhi, C.S. Tang, Supply chain management for extreme conditions: research opportunities, J. Supply Chain Manag. 57, 7–16 (2021) [Google Scholar]
  16. J. Saragih, A. Tarigan, E.F. Silalahi, Supply chain operational capability and supply chain operational performance: does the supply chain management and supply chain integration matters, Int. J Sup. Chain. Mgt. 9, 1222–1229 (2020) [Google Scholar]
  17. T.J. Pettit, K.L. Croxton, J. Fiksel, The evolution of resilience in supply chain management: a retrospective on ensuring supply chain resilience, J. Bus. Logist. 40, 56–65 (2019) [CrossRef] [Google Scholar]
  18. R. Cole, M. Stevenson, J. Aitken, Blockchain technology: implications for operations and supply chain management, Supply Chain Manag. Int. J. 24, 469–483 (2019) [Google Scholar]
  19. C.W. Craighead, D.J. Ketchen Jr, J.L. Darby, Pandemics and supply chain management research: toward a theoretical toolbox, Decis. Sci. 51, 838–866 (2020) [Google Scholar]
  20. E. Hofmann, H. Sternberg, H. Chen, Supply chain management and Industry 4.0: conducting research in the digital age, Int. J. Phys. Distrib. Logist. Manag. 49, 945–955 (2019) [Google Scholar]
  21. R. Sharma, A. Shishodia, A. Gunasekaran, The role of artificial intelligence in supply chain management: mapping the territory, Int. J. Prod. Res. 60, 7527–7550 (2022) [CrossRef] [Google Scholar]
  22. X.M. Yuan, A. Xue, Supply chain 4.0: new generation of supply chain management, Logistics, 7, 9 (2023) [Google Scholar]
  23. J. Holmström, M. Holweg, B. Lawson, The digitalization of operations and supply chain management: theoretical and methodological implications, J. Oper. Manag. 65, 728–734 (2019) [Google Scholar]
  24. J. Zhao, S. Cui, Z. Xu, Research on the closed-loop supply chain of intelligent products considering government subsidies in the context of the internet of things, Discov. Internet Things 5, 34 (2025) [Google Scholar]
  25. M.M. Queiroz, R. Telles, S.H. Bonilla, Blockchain and supply chain management integration: a systematic review of the literature, Supply Chain Manag. Int. J. 25, 241–254 (2020) [Google Scholar]
  26. H. Ge, M. Gómez, C. Peters, Modeling and optimizing the beef supply chain in the Northeastern US, Agric. Econ. 53, 702–718 (2022) [Google Scholar]
  27. E. Dada, S. Joseph, D. Oyewola, Application of grey wolf optimization algorithm: recent trends, issues, and possible horizons, Gazi Univ. J. Sci. 35, 485–504 (2022) [Google Scholar]
  28. I. Sharma, V. Kumar, S. Sharma, A comprehensive survey on grey wolf optimization, Recent Adv. Comput. Sci. Commun. (Formerly: Recent Patents on Computer Science), 15, 323–333 (2023) [Google Scholar]
  29. H. Shambayati, M. Shafiei Nikabadi, S.M.A. Khatami Firouzabadi, Optimization of virtual closed-loop supply chain under uncertainty: application of IoT, Kybernetes, 52, 1745–1777 (2023) [Google Scholar]
  30. W. Deng, J. Xu, H. Zhao, A novel gate resource allocation method using improved PSO-based QEA, IEEE Trans. Intell. Transp. Syst. 23, 1737–1745 (2020) [Google Scholar]
  31. P.K. Keserwani, M.C. Govil, E.S. Pilli, An optimal intrusion detection system using GWO-CSA-DSAE model, Cyber-Phys. Syst. 7, 197–220 (2020) [Google Scholar]
  32. D. Karami, Supply chain network design using particle swarm optimization (PSO) algorithm, Int. J. Indust. Eng. Oper. Res. 4, 1–8 (2022) [Google Scholar]
  33. G. Baryannis, S. Validi, S. Dani, Supply chain risk management and artificial intelligence: state of the art and future research directions, Int. J. Prod. Res. 57, 2179–2202 (2019) [Google Scholar]
  34. B. Gammelgaard, K. Nowicka, Next generation supply chain management: the impact of cloud computing, J. Enterp. Inf. Manag. 37, 1140–1160 (2024) [Google Scholar]
  35. Y. Cao, Z. Zhang, F. Cheng, Trajectory optimization for high-speed trains via a mixed integer linear programming approach, IEEE Trans. Intell. Transp. Syst. 23, 17666–17676 (2022) [Google Scholar]
  36. J. Qian, Z. Zhang, L. Shi, An assembly timing planning method based on knowledge and mixed integer linear programming, J. Intell. Manuf. 34, 429–453 (2023) [Google Scholar]
  37. R.D. Gunawan, R. Napianto, R.I. Borman, Implementation of Dijkstra's algorithm in determining the shortest path (Case study: specialist doctor search in Bandar Lampung), Int. J. Inf. Syst. Comput. Sci. 3, 98–106 (2019) [Google Scholar]
  38. A. Sedeño-Noda, M. Colebrook, A biobjective Dijkstra algorithm, Eur. J. Oper. Res. 276, 106–118 (2019) [CrossRef] [Google Scholar]

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