참고문헌
- F. Zhang, S. Nguyen, Y. Mei, M. Zhang, "Genetic Programming for Production Scheduling: An Evolutionary Learning Approach". Springer Singapore. https://doi.org/10.1007/978-981-16-4859-5
- S. C. Graves, "A Review of Production Scheduling. Operations Research", 29(4), 646-675. 1981. https://doi.org/10.1287/opre.29.4.646
- J. Blazewicz, K. H. Ecker, E. Pesch, G. Schmidt, M. Sterna, J. Weglarz, "Handbook on Scheduling: From Theory to Practice". Springer International Publishing. 2009. https://doi.org/10.1007/978-3-319-99849-7
- Y. Suppiah, T. Bhuvaneswari, P. Shen Yee, N. Wei Yue, C. Mun Horng. "Scheduling Single Machine Problem to Minimize Completion Time". TEM Journal, 552-556. 2022. https://doi.org/10.18421/TEM112-08
- S. Nguyen, M. Zhang, M. Johnston, K.C. Tan, "Automatic programming via iterated local search for dynamic job shop scheduling". IEEE Transactions on Cybernetics, 45(1), 1-14. 2015. https://doi.org/10.1109/TCYB.2014.2317488
- F. Zhang, Y. Mei, S. Nguyen, M. Zhang, "Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling". IEEE Transactions on Cybernetics, 51(4), 1797-1811. 2021. https://doi.org/10.1109/TCYB.2020.3024849
- N. Daneshjo, P. Malega, "Changing of the Maintenance System in the Production Plant with the Application of Predictive Maintenance. TEM Journal", 434-441. 2020. https://doi.org/10.18421/TEM92-03
- T.P. Carvalho, F.A.A.M.N. Soares, R. Vita, R. da P. Francisco, J. P. Basto, S.G.S. Alcala, "A systematic literature review of machine learning methods applied to predictive maintenance". Computers & Industrial Engineering, 137, 106024. 2019. https://doi.org/10.1016/j.cie.2019.106024
- V. Gunnerud, B. Foss, "Oil production optimization-A piecewise linear model, solved with two decomposition strategies". Computers & Chemical Engineering, 34, 1803-1812. 2010. https://doi.org/10.1016/j.compchemeng.2009.10.019
- K. Margellos, P.Goulart, P., & Lygeros, J. (2014). On the Road Between Robust Optimization and the Scenario Approach for Chance Constrained Optimization Problems. Automatic Control, IEEE Transactions On, 59, 2258-2263. https://doi.org/10.1109/TAC.2014.2303232
- E. Lughofer, M. Sayed-Mouchaweh, "Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications". Springer International Publishing. 2019. https://doi.org/10.1007/978-3-030-05645-2
- R.K. Mobley, "An introduction to predictive maintenance (2nd ed)". Butterworth-Heinemann. 2002.
- I. Paprocka, D. Krenczyk, A. Burduk, "The Method of Production Scheduling with Uncertainties Using the Ants Colony Optimisation". Applied Sciences, 11(1), 171. 2021. https://doi.org/10.3390/app11010171
- S. Zhai, M.G. Kandemir, G. Reinhart, "Predictive maintenance integrated production scheduling by applying deep generative prognostics models: Approach, formulation and solution". Production Engineering, 16(1), 65-88. 2022. https://doi.org/10.1007/s11740-021-01064-0
- B. Kitchenham, "Procedures for Performing Systematic Reviews". Keele, UK, Keele Univ., 33. 2004.
- C. Sohrabi, T, Franchi, G. Mathew, A. Kerwan, M. Nicola, M. Griffin, M. Agha, R. Agha, "PRISMA 2020 statement: What's new and the importance of reporting guidelines". International Journal of Surgery, 88, 105918. 2021. https://doi.org/10.1016/j.ijsu.2021.105918
- M.J. Page, J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, L Shamseer, J. M. Tetzlaff, E. A. Akl, S. E. Brennan, R. Chou, J. Glanville, J.M. Grimshaw, A. Hrobjartsson, M. M. Lalu, T. Li, E. W. Loder, E. Mayo-Wilson, S. McDonald, D. Moher, "The PRISMA 2020 statement: An updated guideline for reporting systematic reviews". International Journal of Surgery, 88, 105906. 2021. https://doi.org/10.1016/j.ijsu.2021.105906
- P. Tugwell, D. Tovey, "PRISMA 2020. Journal of Clinical Epidemiology", 134, A5-A6. 2021. https://doi.org/10.1016/j.jclinepi.2021.04.008
- B. Gundogan, N. Dowlut, S. Rajmohan, M. R. Borrelli, M. Millip, C. Iosifidis, Y. Z. Udeaja, G. Mathew, A. Fowler, R. Agha, "Assessing the compliance of systematic review articles published in leading dermatology journals with the PRISMA statement guidelines: A systematic review". JAAD International, 1(2), 157-174. 2020. https://doi.org/10.1016/j.jdin.2020.07.007
- H. M. Hashemian, W. C. Bean, "State-of-the-Art Predictive Maintenance Techniques". IEEE Transactions on Instrumentation and Measurement, 60(10), 3480-3492. 2011. https://doi.org/10.1109/TIM.2009.2036347
- T. Kufner, F. Dopper, D. Muller, A. G. Trenz, "Predictive Maintenance: Using Recurrent Neural Networks for Wear Prognosis in Current Signatures of Production Plants". International Journal of Mechanical Engineering and Robotics Research, 583-591. 2021. https://doi.org/10.18178/ijmerr.10.11.583-591
- I. Paprocka, W. M. Kempa, "Model of Production System Evaluation with the Influence of FDM Machine Reliability and Process-Dependent Product Quality". Materials, 14(19), 5806. 2021. https://doi.org/10.3390/ma14195806
- E. Arena, A. Corsini, R. Ferulano, D. A. Iuvara, E. S. Miele, L. Ricciardi Celsi, N. A. Sulieman, M. Villari, "Anomaly Detection in Photovoltaic Production Factories via Monte Carlo Pre-Processed Principal Component Analysis". Energies, 14(13), 3951. 2021. https://doi.org/10.3390/en14133951
- I.Aslanidou, M. Rahman, V. Zaccaria, K. G. Kyprianidis, "Micro Gas Turbines in the Future Smart Energy System: Fleet Monitoring, Diagnostics, and System Level Requirements". Frontiers in Mechanical Engineering, 7, 51. 2021. https://doi.org/10.3389/fmech.2021.676853
- A. Ladj, F. B.-S. Tayeb, C. Varnier, "Hybrid of metaheuristic approaches and fuzzy logic for the integrated flowshop scheduling with predictive maintenance problem under uncertainties". European Journal of Industrial Engineering. 2021. https://www.inderscienceonline.com/doi/abs/10.1504/EJIE.2021.117325
- Eppinger Thomas, Longwell Glenn, Mas Peter, Goodheart Kevin, Badiali Umberto, & Aglave Ravindra, "Increase Food Production Efficiency Using the Executable Digital Twin (xdt)". Chemical Engineering Transactions, 87, 37-42. 2021. https://doi.org/10.3303/CET2187007
- I. Paprocka, W. M. Kempa, G. Cwikla, "Predictive Maintenance Scheduling with Failure Rate Described by Truncated Normal Distribution". Sensors, 20(23), 6787. 2020. https://doi.org/10.3390/s20236787
- A. Essien, C. Giannetti, "A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders". IEEE Transactions on Industrial Informatics, 16(9), 6069-6078. 2020. https://doi.org/10.1109/TII.2020.2967556
- C. Morariu, O. Morariu, S. Raileanu, T. Borangiu, "Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems". Computers in Industry, 120, 103244. 2020. https://doi.org/10.1016/j.compind.2020.103244
- N. Daneshjo, P. Malega, "Changing of the Maintenance System in the Production Plant with the Application of Predictive Maintenance". TEM Journal, 434-441. 2020. https://doi.org/10.18421/TEM92-03
- M. Ghaleb, S. Taghipour, M. Sharifi, H. Zolfagharinia, "Integrated production and maintenance scheduling for a single degrading machine with deterioration-based failures". Computers & Industrial Engineering, 143, 106432. 2020. https://doi.org/10.1016/j.cie.2020.106432
- F. C. M. Thom, J. R. B. Zoghbi, M. S. da R. Freitas, G. R. Sisquini, "Dynamic risk calculation model applied to gas compressor". REM - International Engineering Journal, 73, 33-41. 2019. https://doi.org/10.1590/0370-44672018730192
- G. Herranz, A. Antolinez, J. Escartin, A. Arregi, J. Gerrikagoitia, "Machine Tools Anomaly Detection Through Nearly Real-Time Data Analysis". Journal of Manufacturing and Materials Processing, 3(4), 97. 2019. https://doi.org/10.3390/jmmp3040097
- S. Antomarioni, O. Pisacane, D. Potena, M. Bevilacqua, F. E. Ciarapica, C. Diamantini, "A predictive association rule-based maintenance policy to minimize the probability of breakages: Application to an oil refinery". The International Journal of Advanced Manufacturing Technology, 105(9), 3661-3675. 2019. https://doi.org/10.1007/s00170-019-03822-y
- Q. Liu, M. Dong, F. F. Chen, W. Lv, C. Ye, "Single-machine-based joint optimization of predictive maintenance planning and production scheduling". Robotics and Computer-Integrated Manufacturing, 55, 173-182. 2019. https://doi.org/10.1016/j.rcim.2018.09.007
- D. F. Hesser, B. Markert, "Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks". Manufacturing Letters, 19, 1-4. 2019. https://doi.org/10.1016/j.mfglet.2018.11.001
- S. S. Baliarsingh, "Wear particle analysis of an antifriction bearing". International Journal of Mechanical Engineering and Technology, 9(3), 684-699.
- Q. Qi, F. Tao, "Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison". IEEE Access, 6, 3585-3593. 2018. https://doi.org/10.1109/ACCESS.2018.2793265
- H. Rodseth, P. Schjolberg, A. Marhaug, "Deep digital maintenance". Advances in Manufacturing, 5(4), 299-310. 2017. https://doi.org/10.1007/s40436-017-0202-9
- H. Peng, G.-J. van Houtum, "Joint optimization of condition-based maintenance and production lot-sizing". European Journal of Operational Research, 253(1), 94-107. 2016. https://doi.org/10.1016/j.ejor.2016.02.027
- L. Waltersmann, S. Kiemel, J. Stuhlsatz, A. Sauer, R. Miehe, "Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies-A Comprehensive Review". Sustainability, 13(12), 6689. 2021. https://doi.org/10.3390/su13126689
- S. Dutta, N. S. K. Reddy, "Adaptive and noncyclic preventive maintenance to augment production activities". Journal of Quality in Maintenance Engineering, 27(1), 92-106. 2020. https://doi.org/10.1108/JQME-03-2018-0017
- S. Martins, M. L. R. Varela, J. Machado, "Development of a System for Supporting Industrial Management". In V. Ivanov, J. Trojanowska, J. Machado, O. Liaposhchenko, J. Zajac, I. Pavlenko, M. Edl, & D. Perakovic (Eds.), Advances in Design, Simulation and Manufacturing II (pp. 209-215). Springer International Publishing. 2020. https://doi.org/10.1007/978-3-030-22365-6_21
- H. Yihai, G. Changchao, H. Xiao, C. Jiaming, C. Zhaoxiang, "Mission reliability modeling for multi-station manufacturing system based on Quality State Task Network". 2017. https://journals.sagepub.com/doi/10.1177/1748006X17728599
- N. Do, "Integration of design and manufacturing data to support personal manufacturing based on 3D printing services". The International Journal of Advanced Manufacturing Technology, 90(9), 3761-3773. 2017. https://doi.org/10.1007/s00170-016-9688-8
- A. Legarretaetxebarria, M. Quartulli, I. Olaizola, M. Serrano, "Optimal scheduling of manufacturing processes across multiple production lines by polynomial optimization and bagged bounded binary knapsack". International Journal on Interactive Design and Manufacturing (IJIDeM), 11(1), 83-91. 2017. https://doi.org/10.1007/s12008-016-0323-6
- P. Ershun, L. Wenzhu, X. Lifeng, "A joint model of production scheduling and predictive maintenance for minimizing job tardiness". SpringerLink. Retrieved October 31, 2021, from https://link.springer.com/article/10.1007%2Fs00170-011-3652-4
- J.-Y. Shiau, "Effectivity date analysis and scheduling". International Journal of Production Research, 49(10), 2771-2791. 2011. https://doi.org/10.1080/00207541003713017
- T. Schlegel, S. Thiel, M. Foursa, F. Meo, J. Larranaga, J. A. Ibarbia, G. Haidegger, I. Mezgar, I. Paniti, A. H. Praturlon, J. Canou, "Smart connected and interactive production control in a distributed environment". International Journal of Computer Aided Engineering and Technology, 3(3/4), 322. 2011. https://doi.org/10.1504/IJCAET.2011.040051
- H. Dehghan Shoorkand, M. Nourelfath, and A. Hajji, "A deep learning approach for integrated production planning and predictive maintenance," International Journal of Production Research, vol. 61, no. 23, pp. 7972-7991, Dec. 2023, doi: 10.1080/00207543.2022.2162618
- M. Arena, V. Di Pasquale, R. Iannone, S. Miranda, and S. Riemma, "A maintenance driven scheduling cockpit for integrated production and maintenance operation schedule," Adv. Manuf., vol. 10, no. 2, pp. 205-219, Jun. 2022, doi: 10.1007/s40436-021-00380-z.
- T. Zonta, C. A. da Costa, F. A. Zeiser, G. de Oliveira Ramos, R. Kunst, and R. da Rosa Righi, "A predictive maintenance model for optimizing production schedule using deep neural networks," Journal of Manufacturing Systems, vol. 62, pp. 450-462, Jan. 2022, doi: 10.1016/j.jmsy.2021.12.013.
- A. Salmasnia and S. Dehghani, "A production-inventory model under quality-maintenance policy with rework process in the presence of random failures and multiple assignable causes," International Journal of Modelling and Simulation, vol. 43, no. 6, pp. 832-848, Nov. 2023, doi: 10.1080/02286203.2022.2127053.
- G. Bencheikh, A. Letouzey, and X. Desforges, "An approach for joint scheduling of production and predictive maintenance activities," Journal of Manufacturing Systems, vol. 64, pp. 546-560, Jul. 2022, doi: 10.1016/j.jmsy.2022.08.005.
- V. K, S. S, V. P, S. R, and G. Di Bona, "Availability Analysis of the Critical Production System in SMEs Using the Markov Decision Model," Mathematical Problems in Engineering, vol. 2022, p. e6026984, Sep. 2022, doi: 10.1155/2022/6026984.
- T. Xia et al., "Collaborative production and predictive maintenance scheduling for flexible flow shop with stochastic interruptions and monitoring data," Journal of Manufacturing Systems, vol. 65, pp. 640-652, Oct. 2022, doi: 10.1016/j.jmsy.2022.10.016.
- J. Wodecki, P. Krot, A. Wroblewski, K. Chudy, and R. Zimroz, "Condition Monitoring of Horizontal Sieving Screens-A Case Study of Inertial Vibrator Bearing Failure in Calcium Carbonate Production Plant," Materials, vol. 16, no. 4, Art. no. 4, Jan. 2023, doi: 10.3390/ma16041533.
- X. Li, D. Chang, and Y. Sun, "Data-driven predictive maintenance method for digital welding machines," Materia (Rio J.), vol. 28, p. e20230096, May 2023, doi: 10.1590/1517-7076-RMAT-2023-0096.
- K. S. H. Ong, W. Wang, D. Niyato, and T. Friedrichs, "Deep-Reinforcement-Learning-Based Predictive Maintenance Model for Effective Resource Management in Industrial IoT," IEEE Internet of Things Journal, vol. 9, no. 7, pp. 5173-5188, Apr. 2022, doi: 10.1109/JIOT.2021.3109955.
- H. Zermane and A. Drardja, "Development of an efficient cement production monitoring system based on the improved random forest algorithm," Int J Adv Manuf Technol, vol. 120, no. 3, pp. 1853-1866, May 2022, doi: 10.1007/s00170-022-08884-z.
- L. Romano, M. Godio, P. Johannesson, F. Bruzelius, T. Ghandriz, and B. Jacobson, "Development of the Vastra Gotaland Operating Cycle for Long-Haul Heavy-Duty Vehicles," IEEE Access, vol. 11, pp. 73268-73302, 2023, doi: 10.1109/ACCESS.2023.3295989.
- H. Gao, Y. Li, Y. Zhao, and Y. Song, "Dual Channel Feature Attention-Based Approach for RUL Prediction Considering the Spatiotemporal Difference of Multisensor Data," IEEE Sensors Journal, vol. 23, no. 8, pp. 8514-8525, Apr. 2023, doi: 10.1109/JSEN.2023.3246595.
- A. Bagozi, D. Bianchini, and A. Rula, "Multi-perspective Data Modelling in Cyber Physical Production Networks: Data, Services and Actors," Data Sci. Eng., vol. 7, no. 3, pp. 193-212, Sep. 2022, doi: 10.1007/s41019-022-00194-4.
- Y. Shin et al., "Multiple linear regression and GRU model for the online prediction of catalyst activity and lifetime in counter-current continuous catalytic reforming," Korean J. Chem. Eng., vol. 40, no. 6, pp. 1284-1296, Jun. 2023, doi: 10.1007/s11814-023-1378-2.
- L.-C. Kung and Z.-Y. Liao, "Optimization for a Joint Predictive Maintenance and Job Scheduling Problem With Endogenous Yield Rates," IEEE Transactions on Automation Science and Engineering, vol. 19, no. 3, pp. 1555-1566, Jul. 2022, doi: 10.1109/TASE.2022.3173822.
- S. Zhai, M. G. Kandemir, and G. Reinhart, "Predictive maintenance integrated production scheduling by applying deep generative prognostics models: approach, formulation and solution," Prod. Eng. Res. Devel., vol. 16, no. 1, pp. 65-88, Feb. 2022, doi: 10.1007/s11740-021-01064-0.
- M. M. Hamasha et al., "Strategical selection of maintenance type under different conditions," Sci Rep, vol. 13, no. 1, Art. no. 1, Sep. 2023, doi: 10.1038/s41598-023-42751-5.