{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:25:47Z","timestamp":1781648747303,"version":"3.54.5"},"reference-count":21,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Serbian Ministry of Science, Technological Development and Innovation","award":["451-03-65\/2024-03\/200123"],"award-info":[{"award-number":["451-03-65\/2024-03\/200123"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Performance-Based Logistics (PBL) frameworks prioritize system availability by optimizing maintenance strategies, with repair rate estimation playing a critical role in predictive maintenance planning. This study proposes a machine learning-based approach for repair rate prediction, leveraging fully connected neural networks (FCNNs) and Long Short-Term Memory (LSTM) networks trained on repair rate samples generated from a stochastic model. The FCNN estimates maximum repair rates, while the LSTM predicts minimum repair rates, capturing both steady-state and sequential dependencies in repair rate variations. By eliminating the need for complex mathematical formulations, the proposed methodology provides a scalable and computationally efficient alternative to traditional stochastic models. Extensive performance evaluations demonstrate that the neural networks achieve higher accuracy and lower computational costs compared to stochastic approaches, making them well-suited for real-time predictive maintenance applications. This research enhances decision-making in maintenance planning, optimizes resource allocation, and improves overall system reliability within PBL frameworks.<\/jats:p>","DOI":"10.3390\/info16121031","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T13:56:34Z","timestamp":1764165394000},"page":"1031","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Neural Network-Based Optimization of Repair Rate Estimation in Performance-Based Logistics Systems"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0787-1816","authenticated-orcid":false,"given":"Milan","family":"Dejanovi\u0107","sequence":"first","affiliation":[{"name":"Faculty of Sciences and Mathematics, University of Pristina, Lole Ribara 29, 32820 Kosovska Mitrovica, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5868-1764","authenticated-orcid":false,"given":"Stefan","family":"Pani\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Sciences and Mathematics, University of Pristina, Lole Ribara 29, 32820 Kosovska Mitrovica, Serbia"},{"name":"Academy of Technical and Art Applied Studies, School of Electrical and Computing Engineering, Vojvode Stepe 283, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6955-9887","authenticated-orcid":false,"given":"Nata\u0161a","family":"Kontrec","sequence":"additional","affiliation":[{"name":"Faculty of Sciences and Mathematics, University of Pristina, Lole Ribara 29, 32820 Kosovska Mitrovica, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0144-5795","authenticated-orcid":false,"given":"Danijel","family":"\u0110o\u0161i\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Sciences and Mathematics, University of Pristina, Lole Ribara 29, 32820 Kosovska Mitrovica, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0569-047X","authenticated-orcid":false,"given":"Sa\u0161a","family":"Milojevi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"ref_1","first-page":"68","article-title":"A Stochastic Model for Estimation of Repair Rate for System Operating under Performance-Based Logistics","volume":"20","author":"Kontrec","year":"2018","journal-title":"Ekspl. Niezawodn.\u2014Maint. Reliab."},{"key":"ref_2","first-page":"24","article-title":"Reliability-Based Model for Optimizing Resources in the Railway Vehicles Maintenance","volume":"11","author":"Osman","year":"2024","journal-title":"Mech. Eng. J."},{"key":"ref_3","first-page":"38","article-title":"Artificial Neural Network-Based Repair and Maintenance Cost Estimation Model for Rice Combine Harvesters","volume":"16","author":"Numsong","year":"2023","journal-title":"Int. J. Agric. Eng. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4277","DOI":"10.1007\/s00170-021-08551-9","article-title":"Selecting an Appropriate Supervised Machine Learning Algorithm for Predictive Maintenance","volume":"119","author":"Ouadah","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3011","DOI":"10.1007\/s00521-022-07667-7","article-title":"Weibull Recurrent Neural Networks for Failure Prognosis Using Histogram Data","volume":"35","author":"Dhada","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kontrec, N., Pani\u0107, S., Pani\u0107, B., Markovi\u0107, A., and Sto\u0161ovi\u0107, D. (2021). Mathematical Approach for System Repair Rate Analysis Used in Maintenance Decision Making. Axioms, 10.","DOI":"10.3390\/axioms10020096"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1007\/s13198-022-01843-7","article-title":"Condition-Based Maintenance Using Machine Learning and the Role of Interpretability: A Review","volume":"15","author":"Sharma","year":"2024","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s11740-021-01064-0","article-title":"Predictive Maintenance Integrated Production Scheduling by Applying Deep Generative Prognostics Models: Approach, Formulation and Solution","volume":"16","author":"Zhai","year":"2022","journal-title":"Prod. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jahangard, M., Xie, Y., and Feng, Y. (2025). Leveraging Machine Learning and Optimization Models for Enhanced Seaport Efficiency. Marit. Econ. Logist., 1\u201342.","DOI":"10.1057\/s41278-024-00309-w"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7549","DOI":"10.1007\/s10462-022-10355-6","article-title":"Autonomous Learning for Fuzzy Systems: A Review","volume":"56","author":"Gu","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"100174","DOI":"10.1016\/j.dajour.2023.100174","article-title":"A Predictive Maintenance Model Using Long Short-Term Memory Neural Networks and Bayesian Inference","volume":"6","author":"Pagano","year":"2023","journal-title":"Decis. Anal. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108285","DOI":"10.1016\/j.engappai.2024.108285","article-title":"A Survey of Deep Learning-Driven Architecture for Predictive Maintenance","volume":"133","author":"Li","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_13","first-page":"200501","article-title":"Systematic Review of Predictive Maintenance Practices in the Manufacturing Sector","volume":"26","author":"Benhanifia","year":"2025","journal-title":"Intell. Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1108\/ECAM-11-2023-1194","article-title":"A LSTM Algorithm-Driven Deep Learning Approach to Estimating Repair and Maintenance Costs of Apartment Buildings","volume":"31","author":"Kim","year":"2024","journal-title":"Eng. Constr. Archit. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Aminzadeh, A., Sattarpanah Karganroudi, S., Majidi, S., Dabompre, C., Azaiez, K., Mitride, C., and S\u00e9n\u00e9chal, E. (2025). A machine learning implementation to predictive maintenance and monitoring of industrial compressors. Sensors, 25.","DOI":"10.3390\/s25041006"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, W., and Li, T. (2025). Comparison of Deep Learning Models for Predictive Maintenance in Industrial Manufacturing Systems Using Sensor Data. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-08515-z"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"585","DOI":"10.2298\/YJOR240315003K","article-title":"Availability-Based Maintenance Analysis for Systems with Repair Time Threshold","volume":"35","author":"Kontrec","year":"2025","journal-title":"Yugosl. J. Oper. Res."},{"key":"ref_18","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_19","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France."},{"key":"ref_20","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_21","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/12\/1031\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T14:04:07Z","timestamp":1764165847000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/12\/1031"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,26]]},"references-count":21,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["info16121031"],"URL":"https:\/\/doi.org\/10.3390\/info16121031","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,26]]}}}