{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T08:15:10Z","timestamp":1794384910534,"version":"4.9.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T00:00:00Z","timestamp":1776470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"vor","delay-in-days":53,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-49107-9","type":"journal-article","created":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T00:05:31Z","timestamp":1776470731000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing smart factory performance via hybrid scheduling and intelligent resource management"],"prefix":"10.1038","volume":"16","author":[{"given":"Saurabh","family":"Vaidya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gordhan","family":"Jethava","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sweta","family":"Jethava","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,18]]},"reference":[{"key":"49107_CR1","unstructured":"Sakeb, A. A., Gohman, A. M. & Ahmed, A. A. Smart factory design: integrating IoT for better industrial management. Afr. J. Adv. Pure Appl. Sci. (AJAPAS) 418\u2013427 (2023)."},{"key":"49107_CR2","unstructured":"Rath, K.\u00a0C. et al. Enabling the Future of Manufacturing: Integration of Robotics and IoT into Smart Factory Infrastructure in Industry 4.0, 101\u2013128 (CRC Press, 2025)."},{"key":"49107_CR3","unstructured":"Alex, B. & Johnson, M. A framework for IoT-enabled smart manufacturing for energy and resource optimization. arXiv preprint arXiv:2502.03040 (2025) ."},{"key":"49107_CR4","first-page":"1","volume":"2025","author":"FB Shaikat","year":"2025","unstructured":"Shaikat, F. B., Islam, R., Happy, A. T. & Faysal, S. A. Optimization of production scheduling in smart manufacturing environments using machine learning algorithms. Lett. High Energy Phys. 2025, 1\u201310 (2025).","journal-title":"Lett. High Energy Phys."},{"key":"49107_CR5","doi-asserted-by":"publisher","first-page":"130","DOI":"10.22381\/AM2020219","volume":"20","author":"S Rogers","year":"2021","unstructured":"Rogers, S. & Zvarikova, K. Big data-driven algorithmic governance in sustainable smart manufacturing: Robotic process and cognitive automation technologies. Anal. Metaphys. 20, 130\u2013144 (2021).","journal-title":"Anal. Metaphys."},{"key":"49107_CR6","doi-asserted-by":"crossref","unstructured":"Boddapati, V.\u00a0N. Optimizing production efficiency in manufacturing using big data and AI\/ML (2025).","DOI":"10.2139\/ssrn.5080585"},{"key":"49107_CR7","doi-asserted-by":"crossref","unstructured":"Ren, L. et al. Industrial metaverse for smart manufacturing: model, architecture, and applications. IEEE Trans. Cybern. (2024) .","DOI":"10.1109\/TCYB.2024.3372591"},{"issue":"1","key":"49107_CR8","first-page":"8","volume":"7","author":"V Veeramachaneni","year":"2025","unstructured":"Veeramachaneni, V. Edge computing: Architecture, applications, and future challenges in a decentralized era. Recent Trends Comput. Graph. Multimed. Technol. 7(1), 8\u201323 (2025).","journal-title":"Recent Trends Comput. Graph. Multimed. Technol."},{"key":"49107_CR9","volume-title":"Enabling Smart Manufacturing Through Cloud Computing and Cyber-Physical Systems, 123\u2013143","author":"IO Olalere","year":"2025","unstructured":"Olalere, I. O. Enabling Smart Manufacturing Through Cloud Computing and Cyber-Physical Systems, 123\u2013143 (CRC Press, 2025)."},{"issue":"1","key":"49107_CR10","first-page":"1","volume":"4","author":"B Dasu","year":"2019","unstructured":"Dasu, B., Sivakumar, M. & Srinivasarao, R. Interconnected multi-machine power system stabilizer design using whale optimization algorithm. Protect. Control Modern Power Syst. 4(1), 1\u201311 (2019).","journal-title":"Protect. Control Modern Power Syst."},{"key":"49107_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2025.103067","volume":"96","author":"L Qi","year":"2025","unstructured":"Qi, L., Zhang, X., Tan, H., Chen, H. & Wang, Y. A novel open and efficient robot development framework based on data distribution service orchestration for agile manufacturing. Robot. Comput.-Integr. Manuf. 96, 103067 (2025).","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"49107_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2023.102608","volume":"85","author":"S Wang","year":"2024","unstructured":"Wang, S. et al. A deep learning-enhanced digital twin framework for improving safety and reliability in human-robot collaborative manufacturing. Robot. Comput.-Integr. Manuf. 85, 102608 (2024).","journal-title":"Robot. Comput.-Integr. Manuf."},{"issue":"19","key":"49107_CR13","doi-asserted-by":"publisher","first-page":"8504","DOI":"10.3390\/su16198504","volume":"16","author":"M Ryalat","year":"2024","unstructured":"Ryalat, M., Franco, E., Elmoaqet, H., Almtireen, N. & Al-Refai, G. The integration of advanced mechatronic systems into industry 4.0 for smart manufacturing. Sustainability 16(19), 8504 (2024).","journal-title":"Sustainability"},{"issue":"1","key":"49107_CR14","doi-asserted-by":"publisher","first-page":"228","DOI":"10.30574\/wjaets.2025.14.1.0019","volume":"14","author":"A Keshar","year":"2025","unstructured":"Keshar, A. Advancing industrial IoT and industry 4.0 through digital twin technologies: A comprehensive framework for intelligent manufacturing, real-time analytics and predictive maintenance. World J. Adv. Eng. Technol. Sci. 14(1), 228\u2013240 (2025).","journal-title":"World J. Adv. Eng. Technol. Sci."},{"key":"49107_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3525261","author":"WC Chuan","year":"2025","unstructured":"Chuan, W. C. et al. Challenges and opportunities in fog computing scheduling: A literature review. IEEE Access https:\/\/doi.org\/10.1109\/access.2024.3525261 (2025).","journal-title":"IEEE Access"},{"key":"49107_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3447097","author":"D Alsadie","year":"2024","unstructured":"Alsadie, D. A comprehensive review of AI techniques for resource management in fog computing: Trends, challenges and future directions. IEEE Access https:\/\/doi.org\/10.1109\/access.2024.3447097 (2024).","journal-title":"IEEE Access"},{"issue":"5","key":"49107_CR17","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.3390\/s24051511","volume":"24","author":"R Almutairi","year":"2024","unstructured":"Almutairi, R., Bergami, G. & Morgan, G. Advancements and challenges in IoT simulators: A comprehensive review. Sensors 24(5), 1511 (2024).","journal-title":"Sensors"},{"issue":"1","key":"49107_CR18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/spe.3243","volume":"54","author":"SN Srirama","year":"2024","unstructured":"Srirama, S. N. A decade of research in fog computing: Relevance, challenges, and future directions. Softw. Pract. Exp. 54(1), 3\u201323 (2024).","journal-title":"Softw. Pract. Exp."},{"issue":"1","key":"49107_CR19","doi-asserted-by":"publisher","first-page":"43","DOI":"10.24138\/jcomss-2024-0125","volume":"21","author":"A Lalouci","year":"2025","unstructured":"Lalouci, A. & Farah, Z. A distributed algorithm to critical node identification in IoT networks. J. Commun. Softw. Syst. 21(1), 43\u201352 (2025).","journal-title":"J. Commun. Softw. Syst."},{"key":"49107_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3463791","author":"D Alsadie","year":"2024","unstructured":"Alsadie, D. Artificial intelligence techniques for securing fog computing environments: Trends, challenges, and future directions. IEEE Access https:\/\/doi.org\/10.1109\/access.2024.3463791 (2024).","journal-title":"IEEE Access"},{"key":"49107_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3460468","author":"S Alsaleh","year":"2024","unstructured":"Alsaleh, S., Menai, M. E. B. & Al-Ahmadi, S. Federated learning-based model to lightweight IDSS for heterogeneous IoT networks: State-of-the-art, challenges and future directions. IEEE Access https:\/\/doi.org\/10.1109\/ACCESS.2024.3460468 (2024).","journal-title":"IEEE Access"},{"issue":"4","key":"49107_CR22","doi-asserted-by":"publisher","first-page":"2131","DOI":"10.1109\/COMST.2021.3106401","volume":"23","author":"Q Luo","year":"2021","unstructured":"Luo, Q., Hu, S., Li, C., Li, G. & Shi, W. Resource scheduling in edge computing: A survey. IEEE Commun. Surv. Tutor. 23(4), 2131\u20132165 (2021).","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"5","key":"49107_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10586-024-05074-2","volume":"28","author":"S Vaidya","year":"2025","unstructured":"Vaidya, S. & Jethava, G. Elevating manufacturing excellence with multilevel optimization in smart factory cloud computing using hybrid model. Cluster Comput. 28(5), 1\u201325 (2025).","journal-title":"Cluster Comput."},{"issue":"1","key":"49107_CR24","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1109\/TWC.2019.2946140","volume":"19","author":"E Li","year":"2019","unstructured":"Li, E., Zeng, L., Zhou, Z. & Chen, X. Edge AI: On-demand accelerating deep neural network inference via edge computing. IEEE Trans. Wirel. Commun. 19(1), 447\u2013457 (2019).","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"49107_CR25","doi-asserted-by":"crossref","unstructured":"Brandalero, M. et al. AITIA: Embedded AI techniques for embedded industrial applications. In 2020 International Conference on Omni-layer Intelligent Systems (COINS) 1\u20137 (2020).","DOI":"10.1109\/COINS49042.2020.9191672"},{"issue":"2","key":"49107_CR26","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3390\/machines6020023","volume":"6","author":"MM Mabkhot","year":"2018","unstructured":"Mabkhot, M. M., Al-Ahmari, A. M., Salah, B. & Alkhalefah, H. Requirements of the smart factory system: A survey and perspective. Machines 6(2), 23 (2018).","journal-title":"Machines"},{"key":"49107_CR27","doi-asserted-by":"publisher","first-page":"101727","DOI":"10.1109\/ACCESS.2023.3316116","volume":"11","author":"JA Fortoul-Diaz","year":"2023","unstructured":"Fortoul-Diaz, J. A. et al. A smart factory architecture based on industry 4.0 technologies.Open-source software implementation. IEEE Access 11, 101727\u2013101749 (2023).","journal-title":"IEEE Access"},{"issue":"4","key":"49107_CR28","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1109\/MIE.2020.3004975","volume":"14","author":"A Aijaz","year":"2020","unstructured":"Aijaz, A. Private 5G: The future of industrial wireless. IEEE Ind. Electron. Mag. 14(4), 136\u2013145 (2020).","journal-title":"IEEE Ind. Electron. Mag."},{"key":"49107_CR29","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.future.2019.09.039","volume":"111","author":"RO Aburukba","year":"2020","unstructured":"Aburukba, R. O., AliKarrar, M., Landolsi, T. & El-Fakih, K. Scheduling internet of things requests to minimize latency in hybrid fog-cloud computing. Future Gener. Comput. Syst. 111, 539\u2013551 (2020).","journal-title":"Future Gener. Comput. Syst."},{"key":"49107_CR30","doi-asserted-by":"crossref","unstructured":"Jagadish, T., Apte, O. & Pradeep, K. Task scheduling algorithms in fog computing: a comparison and analysis. In 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) 483\u2013488 (2022).","DOI":"10.1109\/ICACRS55517.2022.10029029"},{"key":"49107_CR31","first-page":"50994","volume":"11","author":"M Hosseinzadeh","year":"2023","unstructured":"Hosseinzadeh, M. et al. Task scheduling mechanisms for fog computing: A systematic survey. IEEE Access 11, 50994\u201351017 (2023).","journal-title":"IEEE Access"},{"issue":"3","key":"49107_CR32","first-page":"1502","volume":"26","author":"J Geetha","year":"2022","unstructured":"Geetha, J., Kottur, S. Z., Ganiga, R., Jayalakshmi, D. & Surabhi, T. Analysis of rank-based latency aware fog scheduling using validating internet of things at large scales. Indones. J. Electr. Eng. Comput. Sci. 26(3), 1502\u20131511 (2022).","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"issue":"21","key":"49107_CR33","doi-asserted-by":"publisher","first-page":"11695","DOI":"10.1007\/s00500-022-07489-8","volume":"26","author":"B Duan","year":"2022","unstructured":"Duan, B., Guo, C. & Liu, H. A hybrid genetic-particle swarm optimization algorithm for multi-constraint optimization problems. Soft Comput. 26(21), 11695\u201311711 (2022).","journal-title":"Soft Comput."},{"issue":"4","key":"49107_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s10922-022-09664-6","volume":"30","author":"AS Abohamama","year":"2022","unstructured":"Abohamama, A. S., El-Ghamry, A. & Hamouda, E. Real-time task scheduling algorithm for iot-based applications in the cloud-fog environment. J. Netw. Syst. Manage. 30(4), 54 (2022).","journal-title":"J. Netw. Syst. Manage."},{"key":"49107_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.111142","volume":"151","author":"S Karami","year":"2024","unstructured":"Karami, S., Azizi, S. & Ahmadizar, F. A bi-objective workflow scheduling in virtualized fog-cloud computing using nsga-ii with semi-greedy initialization. Appl. Soft Comput. 151, 111142 (2024).","journal-title":"Appl. Soft Comput."},{"key":"49107_CR36","unstructured":"Canozensoy, f. n.\u00a0u. Factory sensor simulator dataset for industry\u00a05.0: Predictive maintenance & anomaly detection. Kaggle dataset (2025). https:\/\/www.kaggle.com\/datasets\/canozensoy\/industrial-iot-dataset-synthetic."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-49107-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-49107-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-49107-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T23:51:34Z","timestamp":1781049094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-49107-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,18]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["49107"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-49107-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,18]]},"assertion":[{"value":"1 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Yes.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"17956"}}