{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T23:56:45Z","timestamp":1773100605068,"version":"3.50.1"},"reference-count":47,"publisher":"Stowarzyszenie Menedzerow Jakosci i Produkcji","issue":"4","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Effective project management is crucial to the success of any industry, particularly in metalworking, where deadlines, resources, and costs play critical roles. However, accurately predicting project execution times remains a significant challenge, directly impacting companies\u2019 competitiveness and profitability. In this context, the integration of Artificial Intelligence (AI) tools emerges as a promising solution to improve the accuracy of time predictions and optimise project management in the metal-working industry.<\/jats:p>\n                  <jats:p>AI, particularly through techniques such as Machine Learning (ML), has demonstrated significant potential in predicting timeframes for engineering projects. Predictive activity-based models can be trained with historical data to identify patterns and forecast future durations with high accuracy. In the metalworking sector, where projects are often complex and subject to variability, AI can provide notable advantages in terms of precision and efficiency.<\/jats:p>\n                  <jats:p>This study aims to formulate an activity-based model, represented in IDEF0 (part of the Integration Definition for Function Modelling), for predicting activity durations using AI to support project management in the metalworking industry. By applying the principles of the IDEF0 tool, the objective is to develop a robust and adaptable system capable of analysing historical data, environmental factors, project characteristics, and other relevant inputs to produce more accurate time forecasts.<\/jats:p>\n                  <jats:p>With this work, we aim to contribute to the advancement of Project Management (PM) in the metal-working industry, particularly by providing an activity-based model to support the creation of an innovative AI tool for predicting execution times with greater accuracy.<\/jats:p>","DOI":"10.30657\/pea.2025.31.52","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T13:26:30Z","timestamp":1765286790000},"page":"565-579","source":"Crossref","is-referenced-by-count":1,"title":["Activity based model based on AI to support the prediction of activity durations in metalworking project management"],"prefix":"10.30657","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4327-4931","authenticated-orcid":false,"given":"Jos\u00e9","family":"Silva","sequence":"first","affiliation":[{"name":"University of Aveiro , Campus Universit\u00e1rio de Santiago , Aveiro , Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8420-0875","authenticated-orcid":false,"given":"Paulo","family":"\u00c1vila","sequence":"additional","affiliation":[{"name":"ISEP, Polytechnic of Porto , rua Dr. Ant\u00f3nio Bernardino de Almeida , Porto , Portugal"},{"name":"INESC TEC \u2013 Instituto de Engenharia de Sistemas e Computadores , Porto , Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1169-7785","authenticated-orcid":false,"given":"Luiz","family":"Faria","sequence":"additional","affiliation":[{"name":"ISEP, Polytechnic of Porto , rua Dr. Ant\u00f3nio Bernardino de Almeida , Porto , Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9082-3291","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Bastos","sequence":"additional","affiliation":[{"name":"ISEP, Polytechnic of Porto , rua Dr. Ant\u00f3nio Bernardino de Almeida , Porto , Portugal"},{"name":"INESC TEC \u2013 Instituto de Engenharia de Sistemas e Computadores , Porto , Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4225-6525","authenticated-orcid":false,"given":"Lu\u00eds Pinto","family":"Ferreira","sequence":"additional","affiliation":[{"name":"ISEP, Polytechnic of Porto , rua Dr. Ant\u00f3nio Bernardino de Almeida , Porto , Portugal"},{"name":"Associate Laboratory for Energy, Transports and Aerospace (LAETA-INEGI) , Porto , Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5712-9954","authenticated-orcid":false,"given":"H\u00e9lio","family":"Castro","sequence":"additional","affiliation":[{"name":"ISEP, Polytechnic of Porto , rua Dr. Ant\u00f3nio Bernardino de Almeida , Porto , Portugal"},{"name":"INESC TEC \u2013 Instituto de Engenharia de Sistemas e Computadores , Porto , Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4329-6246","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Matias","sequence":"additional","affiliation":[{"name":"University of Aveiro , Campus Universit\u00e1rio de Santiago , Aveiro , Portugal"},{"name":"GOVCOPP \u2013 Unidade de Investiga\u00e7\u00e3o em Governan\u00e7a, Competitividade e Pol\u00edticas P\u00fablicas , Universidade de Aveiro , Aveiro , Portugal"}]}],"member":"12128","published-online":{"date-parts":[[2025,12,6]]},"reference":[{"key":"2026030913114144313_j_pea.2025.31.52_ref_001","doi-asserted-by":"crossref","unstructured":"Almagrabi, A. O., & Khan, R. A., 2025. Optimizing Secure AI Lifecycle Model Management With Innovative Generative AI Strategies. IEEE Access, 13, 12889\u201312920. DOI: 10.1109\/ACCESS.2024.3491373","DOI":"10.1109\/ACCESS.2024.3491373"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_002","doi-asserted-by":"crossref","unstructured":"Amran, T. G., Saraswati, D., & Harahap, E. F., 2019. Evaluating Storage Tank Cap 10000L Manufacturer by Using Lean Project Management. IOP Conference Series: Materials Science and Engineering, 528(1), 012052. DOI: 10.1088\/1757-899X\/528\/1\/012052","DOI":"10.1088\/1757-899X\/528\/1\/012052"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_003","unstructured":"\u00c1vila, P., 2004. Rigorous Resource System Selection Model for Agile\/Virtual Enterprise Design for Complex Products. PhD Thesis."},{"key":"2026030913114144313_j_pea.2025.31.52_ref_004","doi-asserted-by":"crossref","unstructured":"\u00c7ak\u0131t, E., & Da\u011fdeviren, M., 2023. Comparative analysis of machine learning algorithms for predicting standard time in a manufacturing environment. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 37, e2. DOI: 10.1017\/S0890060422000245","DOI":"10.1017\/S0890060422000245"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_005","doi-asserted-by":"crossref","unstructured":"Castro, H., C\u00e2mara, E., \u00c1vila, P., Cruz-Cunha, M., & Ferreira, L., 2024. Artificial Intelligence Models: A literature review addressing Industry 4.0 approach. Procedia Computer Science, 239, 2369\u20132376. DOI: 10.1016\/j.procs.2024.06.430","DOI":"10.1016\/j.procs.2024.06.430"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_006","doi-asserted-by":"crossref","unstructured":"Cempel, W., & D\u0105bal, D., 2014. IDEF0 as a Project Management Tool in the Simulation Modeling and Analysis Process in Emergency Evacuation from Hospital Facility: A Case Study. DOI: 10.1007\/978-3-319-07347-7_11","DOI":"10.1007\/978-3-319-07347-7_11"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_007","doi-asserted-by":"crossref","unstructured":"Chari, A., Stahre, J., B\u00e4rring, M., Despeisse, M., Li, D., Friis, M., M\u00f6rstam, M., & Johansson, B., 2023. Analysing the antecedents to digital platform implementation for resilient and sustainable manufacturing supply chains - An IDEF0 modelling approach. Journal of Cleaner Production, 429, 139598. DOI: 10.1016\/j.jclepro.2023.139598","DOI":"10.1016\/j.jclepro.2023.139598"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_008","doi-asserted-by":"crossref","unstructured":"Chen, Y. C., He, B. H., Lin, S. S., Soeseno, J. H., Tan, D. S., Chen, T. P. C., & Chen, W. C., 2021. Demystifying data and AI for manufacturing: Case studies from a major computer maker. APSIPA Transactions on Signal and Information Processing. DOI: 10.1017\/ATSIP.2021.3","DOI":"10.1017\/ATSIP.2021.3"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_009","doi-asserted-by":"crossref","unstructured":"Chen, Y., Clayton, E. W., Novak, L. L., Anders, S., & Malin, B., 2023. Human-Centered Design to Address Biases in Artificial Intelligence. Journal of Medical Internet Research, 25. DOI: DOI: 10.2196\/43251","DOI":"10.2196\/43251"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_010","doi-asserted-by":"crossref","unstructured":"Dahmani, S., Ben-Ammar, O., & Jebali, A., 2021. Resilient Project Scheduling Using Artificial Intelligence: A Conceptual Framework. In A. Dolgui, A. Bernard, D. Lemoine, G. von Cieminski, & D. Romero (Eds.), Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems, 311\u2013320. Springer International Publishing.","DOI":"10.1007\/978-3-030-85874-2_33"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_011","doi-asserted-by":"crossref","unstructured":"Drobintsev, P. D., Kotlyarov, V. P., Chernorutsky, I. G., Kotlyarova, L. P., & Aleksandrova, O. V., 2017. Approach to adaptive control of technological manufacturing processes of IoT metalworking workshop. 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM), 174\u2013176. DOI: 10.1109\/SCM.2017.7970530","DOI":"10.1109\/SCM.2017.7970530"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_012","unstructured":"Duncan Kimutai Ronoh, D. C. C. K., 2020. Influence of Resource Scheduling On the Performance of Residential Construction Projects in Nairobi City County, Kenya. International Journal of Research and Innovation in Social Science, 4."},{"key":"2026030913114144313_j_pea.2025.31.52_ref_013","doi-asserted-by":"crossref","unstructured":"Egwim, C., Alaka, H., Toriola-Coker, O., Balogun, H., & Sunmola, F., 2021. Applied Artificial Intelligence for Predicting Construction Projects Delay. Machine Learning, 6. DOI: 10.1016\/j.mlwa.2021.100166","DOI":"10.1016\/j.mlwa.2021.100166"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_014","doi-asserted-by":"crossref","unstructured":"Elena Bruni, M., Beraldi, P., Guerriero, F., & Pinto, E., 2011. A scheduling methodology for dealing with uncertainty in construction projects. Engineering Computations, 28(8), 1064\u20131078. DOI: 10.1108\/02644401111179036","DOI":"10.1108\/02644401111179036"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_015","doi-asserted-by":"crossref","unstructured":"Fang, W., Guo, Y., Liao, W., Ramani, K., & Huang, S., 2020. Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach. International Journal of Production Research, 58(9), 2751\u20132766. DOI: 10.1080\/00207543.2019.1602744","DOI":"10.1080\/00207543.2019.1602744"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_016","doi-asserted-by":"crossref","unstructured":"Gil Ruiz, J., Mart\u00ednez, J., & Gonzalez Crespo, R., 2021. The Application of Artificial Intelligence in Project Management Research: A Review. International Journal of Interactive Multimedia and Artificial Intelligence, 6. DOI: 10.9781\/ijimai.2020.12.003","DOI":"10.9781\/ijimai.2020.12.003"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_017","doi-asserted-by":"crossref","unstructured":"Gonzales-Romero, A., Huamani-Martinez, I. J., Quiroz-Flores, J. C., & Diaz-Garay, B. H., 2022. Production Management Model Based on Lean and DDMRP Tools to Increase the Rate of Project Compliance in Manufacturing SMEs in the Metalworking Sector. 2022 8th International Engineering, Sciences and Technology Conference (IESTEC), 38\u201345. DOI: 10.1109\/IESTEC54539.2022.00015","DOI":"10.1109\/IESTEC54539.2022.00015"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_018","doi-asserted-by":"crossref","unstructured":"Hasegawa, H., Lima, R., Mota Junior, V., & Teixeira, R., 2025. Challenges in data collection for enhancing productivity in Brazilian industrial processes. Brazilian Journal of Operations & Production Management, 22, e20252445. DOI: 10.14488\/BJOPM.2445.2025","DOI":"10.14488\/BJOPM.2445.2025"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_019","doi-asserted-by":"crossref","unstructured":"Hassan, A., El-Rayes, K., & Attalla, M., 2021. Optimizing the scheduling of crew deployments in repetitive construction projects under uncertainty. Engineering, Construction and Architectural Management, 28(6), 1615\u20131634. DOI: 10.1108\/ECAM-05-2020-0304","DOI":"10.1108\/ECAM-05-2020-0304"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_020","doi-asserted-by":"crossref","unstructured":"Heinzl, B., Silvina, A., Krause, F., Schwarz, N., Kurniawan, K., Kiesling, E., Pichler, M., & Moser, B., 2024. Towards Integrating Knowledge Graphs into Process-Oriented Human-AI Collaboration in Industry. In P. Bludau, R. Ramler, D. Winkler, & J. Bergsmann (Eds.), Software Quality as a Foundation for Security (pp. 76\u201387). Springer Nature Switzerland.","DOI":"10.1007\/978-3-031-56281-5_5"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_021","doi-asserted-by":"crossref","unstructured":"Hupont, I., & G\u00f3mez, E., 2022. Documenting use cases in the affective computing domain using Unified Modeling Language. 2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII), 1\u20138. DOI: 10.1109\/ACII55700.2022.9953809","DOI":"10.1109\/ACII55700.2022.9953809"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_022","unstructured":"Joseph Kozhaya, 2020., November 16). As enterprises move from experimenting with artificial intelligence (AI) to adopting it in production, AI model lifecycle management is quickly becoming the next frontier in development and research. https:\/\/Www.Ibm.Com\/Blog\/Ai-Model-Lifecycle-Management-Build-Phase\/."},{"key":"2026030913114144313_j_pea.2025.31.52_ref_023","unstructured":"Kesarkar, A., Dabre, C., Agarwal, R., Chavan, Y., & Ruhina Karani, P., 2025. Enhanced Structural Health Monitoring Through LSTM-Enhanced Gradient Boosting Regressor. Computer Engineering, DJ Sanghvi College of Engineering, 8(9)."},{"key":"2026030913114144313_j_pea.2025.31.52_ref_024","doi-asserted-by":"crossref","unstructured":"Kim, D.-Y., Kareem, A. B., Domingo, D., Shin, B.-C., & Hur, J.-W., 2024. Advanced Data Augmentation Techniques for Enhanced Fault Diagnosis in Industrial Centrifugal Pumps. Journal of Sensor and Actuator Networks, 13(5). DOI: 10.3390\/jsan13050060","DOI":"10.3390\/jsan13050060"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_025","doi-asserted-by":"crossref","unstructured":"Krynke, M., 2021. Management optimizing the costs and duration time of the process in the production system. Production Engineering Archives, 27, 163\u2013170. DOI: 10.30657\/pea.2021.27.21","DOI":"10.30657\/pea.2021.27.21"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_026","unstructured":"Kumar Dukhiram Pal, D., Chitta, S., Sri Manoj Bonam, V., Katari, P., & Thota, S., 2023. AI-Assisted Project Management: Enhancing Decision-Making and Forecasting. Journal of Artificial Intelligence Research, 3(2), 146\u2013171. https:\/\/www.thesciencebrigade.com\/JAIR\/article\/view\/333"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_027","doi-asserted-by":"crossref","unstructured":"Luo, J., Wong, S. F., Yang, Z. X., & Wong, P. K., 2011. An integrated modeling framework design to support product process in manufacturing enterprises. Proceedings 2011 International Conference on System Science and Engineering, 455\u2013460. DOI: 10.1109\/ICSSE.2011.5961946","DOI":"10.1109\/ICSSE.2011.5961946"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_028","doi-asserted-by":"crossref","unstructured":"Mahale, Y., Kolhar, S., & More, A. S., 2025. A comprehensive review on artificial intelligence driven predictive maintenance in vehicles: technologies, challenges and future research directions. Discover Applied Sciences, 7(4), 243. DOI: 10.1007\/s42452-025-06681-3","DOI":"10.1007\/s42452-025-06681-3"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_029","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hern\u00e1ndez-Orallo, J., Kull, M., Lachiche, N., Ram\u00edrez-Quintana, M. J., & Flach, P., 2021. CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories. IEEE Transactions on Knowledge and Data Engineering, 33(8), 3048\u20133061. DOI: 10.1109\/TKDE.2019.2962680","DOI":"10.1109\/TKDE.2019.2962680"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_030","doi-asserted-by":"crossref","unstructured":"Monteiro, C., Ferreira, L. P., Fernandes, N. O., Silva, F. J. G., & Amaral, I., 2019. Improving the machining process of the metalwork industry by upgrading operative sequences, standard manufacturing times and production procedure changes. Procedia Manufacturing, 38, 1713\u20131722.","DOI":"10.1016\/j.promfg.2020.01.106"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_031","doi-asserted-by":"crossref","unstructured":"Morariu, C., Morariu, O., R\u0103ileanu, S., & Borangiu, T., 2020. Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems. Computers in Industry, 120, 103244. DOI: 10.1016\/j.compind.2020.103244","DOI":"10.1016\/j.compind.2020.103244"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_032","doi-asserted-by":"crossref","unstructured":"OECD, 2019. Artificial Intelligence in Society. OECD Publishing. DOI: 10.1787\/eedfee77-en","DOI":"10.1787\/eedfee77-en"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_033","doi-asserted-by":"crossref","unstructured":"Palakshappa, A., Maradithaya, S., & V, C., 2025. A Machine Learning Method to improve Supplier Delivery Appointments in Supply Chain Industries: A Case Study. Brazilian Journal of Operations & Production Management, 22, 2040. DOI: 10.14488\/BJOPM.2040.2025","DOI":"10.14488\/BJOPM.2040.2025"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_034","doi-asserted-by":"crossref","unstructured":"Palomino-Valles, A., Tokumori-Wong, M., Castro-Rangel, P., Raymundo-Iba\u00f1ez, C., & Dominguez, F., 2020. TPM Maintenance Management Model Focused on Reliability that Enables the Increase of the Availability of Heavy Equipment in the Construction Sector. IOP Conference Series: Materials Science and Engineering, 796(1), 012008. DOI: 10.1088\/1757-899X\/796\/1\/012008","DOI":"10.1088\/1757-899X\/796\/1\/012008"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_035","doi-asserted-by":"crossref","unstructured":"Park, H., Ji, B., Lee, M., Choi, J., Lee, J., Bang, S. H., & Cho, H., 2017. Conceptual Development Process of Mass-customizable Data Analytics Services for Manufacturing SMEs. In H. L\u00f6dding, R. Riedel, K.-D. Thoben, G. von Cieminski, & D. Kiritsis (Eds.), Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing (pp. 194\u2013201). Springer International Publishing.","DOI":"10.1007\/978-3-319-66923-6_23"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_036","doi-asserted-by":"crossref","unstructured":"Ruschel, E., Rocha Loures, E. de F., & Santos, E. A. P., 2021. Performance analysis and time prediction in manufacturing systems. Computers & Industrial Engineering, 151, 106972. DOI: DOI: 10.1016\/j.cie.2020.106972","DOI":"10.1016\/j.cie.2020.106972"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_037","doi-asserted-by":"crossref","unstructured":"Sarajcev, P., Kunac, A., Petrovi\u0107, G., & Despalatovic, M., 2022. Artificial Intelligence Techniques for Power System Transient Stability Assessment. Energies, 15, 507. DOI: 10.3390\/en15020507","DOI":"10.3390\/en15020507"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_038","unstructured":"Serifi, V., Da\u0161i\u0107, P., Je\u010dmenica, R., & D.Labovi\u0107, 2013. Functional and information modeling of production using IDEF methods. Strojniski Vestnik, 55, 131\u2013140."},{"key":"2026030913114144313_j_pea.2025.31.52_ref_039","unstructured":"Shoushtari, F., Daghighi, A., & Ghafourian, E., 2024. Application of Artificial Intelligence in Project Management. International Journal of Industrial Engineering and Operational Research, 6(2), 49\u201363."},{"key":"2026030913114144313_j_pea.2025.31.52_ref_040","doi-asserted-by":"crossref","unstructured":"Silva, J., \u00c1vila, P., Matias, J., Faria, L., Bastos, J., Ferreira, L., & Castro, H., 2024. Bibliographic review of AI applied to project management and its analysis in the context of the metalworking industry. Procedia CIRP, 130, 177\u2013187. DOI: DOI: 10.1016\/j.procir.2024.10.073","DOI":"10.1016\/j.procir.2024.10.073"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_041","doi-asserted-by":"crossref","unstructured":"Silva, J., \u00c1vila, P., Patr\u00edcio, L., S\u00e1, J. C., Ferreira, L. P., Bastos, J., & Castro, H., 2022. Improvement of planning and time control in the project management of a metalworking industry - case study. Procedia Computer Science, 196, 288\u2013295. DOI: 10.1016\/j.procs.2021.12.016","DOI":"10.1016\/j.procs.2021.12.016"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_042","doi-asserted-by":"crossref","unstructured":"Simion, D., Postolache, F., Fleac\u0103, B., & Fleac\u0103, E., 2024. AI-Driven Predictive Maintenance in Modern Maritime Transport\u2014Enhancing Operational Efficiency and Reliability. Applied Sciences, 14, 9439. DOI: 10.3390\/app14209439","DOI":"10.3390\/app14209439"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_043","doi-asserted-by":"crossref","unstructured":"Szwarcfiter, C., Herer, Y. T., & Shtub, A., 2023. Balancing Project Schedule, Cost, and Value under Uncertainty: A Reinforcement Learning Approach. Algorithms, 16(8). DOI: 10.3390\/a16080395","DOI":"10.3390\/a16080395"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_044","doi-asserted-by":"crossref","unstructured":"Wachnik, B., 2022. Analysis of the use of artificial intelligence in the management of Industry 4.0 projects. The perspective of Polish industry. Production Engineering Archives, 28, 56\u201363. DOI: 10.30657\/pea.2022.28.07","DOI":"10.30657\/pea.2022.28.07"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_045","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhang, Y., Gu, J., & Wang, J., 2022. A Proactive Manufacturing Resources Assignment Method Based on Production Performance Prediction for the Smart Factory. IEEE Transactions on Industrial Informatics, 18(1), 46\u201355. DOI: 10.1109\/TII.2021.3073404","DOI":"10.1109\/TII.2021.3073404"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_046","doi-asserted-by":"crossref","unstructured":"Zennaro, I., Finco, S., Battini, D., & Persona, A., 2019. Big size highly customised product manufacturing systems: a literature review and future research agenda. International Journal of Production Research, 57(15\u201316), 5362\u20135385. DOI: 10.1080\/00207543.2019.1582819","DOI":"10.1080\/00207543.2019.1582819"},{"key":"2026030913114144313_j_pea.2025.31.52_ref_047","doi-asserted-by":"crossref","unstructured":"Zohrehvandi, S., 2024. A Fuzzy Overlapping Project Resource Optimization Model in the Project Construction Industry with a Fractal Approach. Mobile Networks and Applications, 29(2), 545\u2013556. DOI: 10.1007\/s11036-023-02254-z","DOI":"10.1007\/s11036-023-02254-z"}],"container-title":["Production Engineering Archives"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.sciendo.com\/pdf\/10.30657\/pea.2025.31.52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T13:12:04Z","timestamp":1773061924000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.sciendo.com\/article\/10.30657\/pea.2025.31.52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,1]]},"references-count":47,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12,6]]},"published-print":{"date-parts":[[2025,12,1]]}},"alternative-id":["10.30657\/pea.2025.31.52"],"URL":"https:\/\/doi.org\/10.30657\/pea.2025.31.52","relation":{},"ISSN":["2353-7779"],"issn-type":[{"value":"2353-7779","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,1]]}}}