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Research Article

Reinforcement learning based optimal decision making towards product lifecycle sustainability

ORCID Icon, ORCID Icon & ORCID Icon
Pages 1269-1296 | Received 04 Sep 2020, Accepted 31 Dec 2021, Published online: 31 Jan 2022

References

  • Abadi, M., P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, et al. 2016. “TensorFlow: A System for Large-Scale Machine Learning.” In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, 265–283. OSDI’16. USA: USENIX Association.
  • Andriotis, C. P., and K. G. Papakonstantinou. 2020. “Deep Reinforcement Learning Driven Inspection and Maintenance Planning under Incomplete Information and Constraints.” ArXiv, July. arXiv. http://arxiv.org/abs/2007.01380.
  • Badurdeen, F., R. Aydin, and A. Brown. November 2018. “A Multiple Lifecycle-Based Approach to Sustainable Product Configuration Design.” Journal of Cleaner Production 200: 756–769. Elsevier Ltd. 10.1016/j.jclepro.2018.07.317.
  • Bishop, C. M. 2006 Pattern Recognition and Machine Learning. New York: Springer.
  • Bosch-Mauchand, M., F. Belkadi, M. Bricogne, and B. Eynard. 2013. “Knowledge-Based Assessment of Manufacturing Process Performance: Integration of Product Lifecycle Management and Value-Chain Simulation Approaches.” International Journal of Computer Integrated Manufacturing 26 (5): 453–473. doi:10.1080/0951192X.2012.731611. Taylor and Francis Ltd.
  • Cai, B., L. Huang, and M. Xie. 2017. “Bayesian Networks in Fault Diagnosis.“ IEEE Transactions on Industrial Informatics 13 ( 5): 2227–2240. doi:10.1109/TII.2017.2695583.
  • Cai, H., L. D. Xu, B. Xu, C. Xie, S. Qin, and L. Jiang. 2014. “IoT-Based Configurable Information Service Platform for Product Lifecycle Management.” IEEE Transactions on Industrial Informatics 10 (2): 1558–1567. doi:10.1109/TII.2014.2306391.
  • Carpenter, B., A. Gelman, M. D. Hoffman, D. Lee, B. Goodrich, M. Betancourt, M. Brubaker, J. Guo, P. Li, and A. Riddell. 2017. “Stan: A Probabilistic Programming Language.” Journal of Statistical Software 76 (1): 1–32. doi:10.18637/jss.v076.i01.
  • Deisenroth, M. P., D. Fox, and C. E. Rasmussen. 2015. “Gaussian Processes for Data-Efficient Learning in Robotics and Control.“ IEEE Transactions on Pattern Analysis and Machine Intelligence 37 ( 2): 408–423. doi:10.1109/TPAMI.2013.218.
  • Ding, R. X., I. Palomares, X. Wang, G. R. Yang, B. Liu, Y. Dong, E. Herrera-Viedma, and F. Herrera. July 2020. “Large-Scale Decision Making: Characterisation, Taxonomy, Challenges and Future Directions from an Artificial Intelligence and Applications Perspective.” Information Fusion 59: 84–102. Elsevier BV. 10.1016/j.inffus.2020.01.006.
  • Duan, C., C. Deng, A. Gharaei, J. Wu, and B. Wang. 2018. “Selective Maintenance Scheduling under Stochastic Maintenance Quality with Multiple Maintenance Actions.” International Journal of Production Research 56 (23): 7160–7178. doi:10.1080/00207543.2018.1436789. Taylor and Francis Ltd.
  • Duan, Y., J. S. Edwards, and Y. K. Dwivedi. 2019. “Artificial Intelligence for Decision Making in the Era of Big Data – Evolution, Challenges and Research Agenda.” International Journal of Information Management 48 (October): 63–71. doi:10.1016/j.ijinfomgt.2019.01.021. Elsevier Ltd.
  • Ferreira, F., J. Faria, A. Azevedo, and L. A. Marques. 2017. “Product Lifecycle Management in Knowledge Intensive Collaborative Environments: An Application to Automotive Industry.” International Journal of Information Management 37 (1): 1474–1487. doi:10.1016/j.ijinfomgt.2016.05.006. Elsevier Ltd: 1474–1487.
  • Froger, A., M. Gendreau, J. E. Mendoza, É. Pinson, and M. L. Rousseau. 2016. “Maintenance Scheduling in the Electricity Industry: A Literature Review.” European Journal of Operational Research 251 (3): 695–706. doi:10.1016/j.ejor.2015.08.045. Elsevier B.V.
  • Gao, J. X., H. Aziz, P. G. Maropoulos, and W. M. Cheung. 2003. “Application of Product Data Management Technologies for Enterprise Integration.” International Journal of Computer Integrated Manufacturing 16 (7–8): 7–8. doi:10.1080/0951192031000115813. Taylor & Francis Group: 491–500.
  • Geissinger, A., C. Laurell, C. Öberg, and C. Sandström. January 2019. “How Sustainable Is the Sharing Economy? on the Sustainability Connotations of Sharing Economy Platforms.” Journal of Cleaner Production 206: 419–429. Elsevier Ltd. 10.1016/j.jclepro.2018.09.196.
  • Gelman, A., J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin. 2013. Bayesian Data Analysis, 3rd Edition. 675. New York: Chapman and Hall/CRC.
  • Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep Learning. Cambridge, Massachusetts: MIT Press. http://www.deeplearningbook.org
  • Govindan, K., A. Jafarian, and V. Nourbakhsh. October 2019. “Designing A Sustainable Supply Chain Network Integrated with Vehicle Routing: A Comparison of Hybrid Swarm Intelligence Metaheuristics.” Computers & Operations Research 110: 220–235. Pergamon. 10.1016/J.COR.2018.11.013.
  • Guo, Z., Y. Zhang, J. Lv, Y. Liu, and Y. Liu. 2021a. “An Online Learning Collaborative Method for Traffic Forecasting and Routing Optimization.“ IEEE Transactions on Intelligent Transportation Systems 22 ( 10): 6634–6645. doi: 10.1109/TITS.2020.2986158.
  • Guo, Z., Y. Zhang, X. Zhao, and X. Song. 2021b. “CPS-Based Self-Adaptive Collaborative Control for Smart Production-Logistics Systems.“ IEEE Transactions on Cybernetics 51 ( 1): 188–198. doi:10.1109/TCYB.2020.2964301.
  • Gustavsson, E., M. Patriksson, A. B. Strömberg, A. Wojciechowski, and M. Önnheim. 2014. “Preventive Maintenance Scheduling of Multi-Component Systems with Interval Costs.” Computers and Industrial Engineering 76 (October): 390–400. doi:10.1016/j.cie.2014.02.009. Elsevier Ltd.
  • Hatim, Q. Y., C. Saldana, G. Shao, D. B. Kim, K. C. Morris, P. Witherell, S. Rachuri, and S. Kumara. 2020. “A Decision Support Methodology for Integrated Machining Process and Operation Plans for Sustainability and Productivity Assessment.” The International Journal of Advanced Manufacturing Technology 107 (7): 3207–3230. 10.1007/S00170-019-04268-Y.
  • Hwang, H. T., S. H. Lee, H. G. Chi, N. K. Kang, H. B. Kong, J. Lu, and H. Ohk. 2019. “An Evaluation Methodology for 3D Deep Neural Networks Using Visualization in 3D Data Classification.” Journal of Mechanical Science and Technology 33 (3): 3. doi:10.1007/s12206-019-0233-1. Korean Society of Mechanical Engineers: 1333–1339.
  • Jardine, A. K. S., D. Lin, and D. Banjevic. 2006. “A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance.” Mechanical Systems and Signal Processing 20 (7): 1483–1510. doi:10.1016/j.ymssp.2005.09.012.
  • Jarrahi, M. H. 2018. “Artificial Intelligence and the Future of Work: Human-AI Symbiosis in Organizational Decision Making.” Business Horizons 61 (4): 577–586. doi:10.1016/J.BUSHOR.2018.03.007. Elsevier.
  • Kaewunruen, S., and Q. Lian. August 2019. “Digital Twin Aided Sustainability-Based Lifecycle Management for Railway Turnout Systems.” Journal of Cleaner Production 228: 1537–1551. Elsevier Ltd. 10.1016/j.jclepro.2019.04.156.
  • Kermany, D. S., M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, et al. 2018. “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning.” Cell 172 (5): 1122–1131.e9. doi:10.1016/j.cell.2018.02.010. Cell Press.
  • Kiritsis, D., A. Bufardi, and P. Xirouchakis. 2003. “Research Issues on Product Lifecycle Management and Information Tracking Using Smart Embedded Systems.” Advanced Engineering Informatics 17 (3–4): 3–4. doi:10.1016/j.aei.2004.09.005. Elsevier BV: 189–202.
  • Knowles, M., D. Baglee, and S. Wermter. 2011. In Reinforcement Learning for Scheduling of Maintenance, edited by Bramer, M., Petridis, M., and Hopgood, A. Research and Development in Intelligent Systems, Vol. XXVII, 409–422. London: Springer. doi:10.1007/978-0-85729-130-1_31.
  • Längkvist, M., L. Karlsson, and A. Loutfi. 2014. “A Review of Unsupervised Feature Learning and Deep Learning for Time-Series Modeling.” Pattern Recognition Letters 42 (1): 11–24. doi:10.1016/j.patrec.2014.01.008. North-Holland.
  • Lee, S. G., Y. S. Ma, G. L. Thimm, and J. Verstraeten. 2008. “Product Lifecycle Management in Aviation Maintenance, Repair and Overhaul.” Computers in Industry 59 (2–3): 2–3. doi:10.1016/j.compind.2007.06.022. Elsevier: 296–303.
  • Li, B. H., B. C. Hou, W. T. Yu, X. B. Lu, and C. W. Yang. 2017. “Applications of Artificial Intelligence in Intelligent Manufacturing: A Review.” Frontiers of Information Technology and Electronic Engineering. Zhejiang University. doi:10.1631/FITEE.1601885.
  • Li, J., F. Tao, Y. Cheng, and L. Zhao. 2015. “Big Data in Product Lifecycle Management.” The International Journal of Advanced Manufacturing Technology 81: 667–684. doi:10.1007/s00170-015-7151-x. .
  • Li, Z., S. Zhong, and L. Lin. 2019. “An Aero-Engine Life-Cycle Maintenance Policy Optimization Algorithm: Reinforcement Learning Approach.” Chinese Journal of Aeronautics 32 (9): 2133–2150. doi:10.1016/j.cja.2019.07.003. Chinese Journal of Aeronautics.
  • Lim, K. Y. H., P. Zheng, and C. H. Chen. 2020. “A State-of-the-Art Survey of Digital Twin: Techniques, Engineering Product Lifecycle Management and Business Innovation Perspectives.” Journal of Intelligent Manufacturing 31 (6): 1313–1337. doi:10.1007/s10845-019-01512-w.
  • Litjens, G., T. Kooi, B. Ehteshami Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez. 2017. “A Survey on Deep Learning in Medical Image Analysis.” Medical Image Analysis 42: 60–88. doi:10.1016/j.media.2017.07.005.
  • Liu, X. L., W. M. Wang, H. Guo, A. V. Barenji, Z. Li, and G. Q. Huang. June 2020a. “Industrial Blockchain Based Framework for Product Lifecycle Management in Industry 4.0.” Robotics and Computer-Integrated Manufacturing 63: 101897. Elsevier Ltd: 101897. 10.1016/j.rcim.2019.101897.
  • Liu, Y., Y. Zhang, S. Ren, M. Yang, Y. Wang, and D. Huisingh. March 2020b. “How Can Smart Technologies Contribute to Sustainable Product Lifecycle Management?” Journal of Cleaner Production 249: 119423. Elsevier Ltd. 10.1016/j.jclepro.2019.119423.
  • Lou, S., Y. Feng, H. Zheng, Y. Gao, and J. Tan. 2018. “Data-Driven Customer Requirements Discernment in the Product Lifecycle Management via Intuitionistic Fuzzy Sets and Electroencephalogram.” Journal of Intelligent Manufacturing 31 (7): 1–16. doi:10.1007/s10845-018-1395-x. Springer New York LLC.
  • Ma, Y., K. Rong, D. Mangalagiu, T. F. Thornton, and D. Zhu. July 2018. “Co-Evolution between Urban Sustainability and Business Ecosystem Innovation: Evidence from the Sharing Mobility Sector in Shanghai.” Journal of Cleaner Production 188: 942–953. Elsevier Ltd. 10.1016/j.jclepro.2018.03.323.
  • MacCarthy, B. L., and R. C. Pasley. 2021. “Group Decision Support for Product Lifecycle Management.” International Journal of Production Research 59 (16): 5050–5067. June. Taylor and Francis Ltd. doi:10.1080/00207543.2020.1779372.
  • Matsokis, A., and D. Kiritsis. 2010. “An Ontology-Based Approach for Product Lifecycle Management.” Computers in Industry 61 (8): 787–797. doi:10.1016/J.COMPIND.2010.05.007. Elsevier.
  • Meng, K., Y. Cao, X. Peng, V. Prybutok, and K. Youcef-Toumi. November 2020. “Smart Recovery Decision Making for End-of-Life Products in the Context of Ubiquitous Information and Computational Intelligence.” Journal of Cleaner Production 272: 122804. Elsevier. 10.1016/J.JCLEPRO.2020.122804.
  • Nezhad, M. Z., N. Sadati, K. Yang, and D. Zhu. January 2019. “A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer.” Expert Systems with Applications 115: 16–26. Elsevier Ltd. 10.1016/j.eswa.2018.07.070.
  • Nishant, R., M. Kennedy, and J. Corbett. August 2020. “Artificial Intelligence for Sustainability: Challenges, Opportunities, and a Research Agenda.” International Journal of Information Management 53: 102104. Pergamon: 102104. 10.1016/J.IJINFOMGT.2020.102104.
  • O’Leary, D. E. 2013. “Artificial Intelligence and Big Data.” IEEE Intelligent Systems 28 (2): 96–99. doi:10.1109/MIS.2013.39.
  • Pomerol, J. C. 1997. “Artificial Intelligence and Human Decision Making.” European Journal of Operational Research 99 (1): 3–25. doi:10.1016/S0377-2217(96)00378-5. Elsevier.
  • Poole, D. L., and A. K. Mackworth. 2017. Artificial Intelligence: Foundations of Computational Agents. 2nd ed. Cambridge: Cambridge University Press.
  • Powell, W. B., and H. Topaloglu. 2006. Approximate Dynamic Programming for Large-Scale Resource Allocation Problems Models, Methods, and Applications for Innovative Decision Making (Informs)doi:10.1287/educ.1063.0027
  • Ren, S., Y. Zhang, Y. Liu, T. Sakao, D. Huisingh, and C. M. V. B. Almeida. 2019. “A Comprehensive Review of Big Data Analytics Throughout Product Lifecycle to Support Sustainable Smart Manufacturing: A Framework, Challenges and Future Research Directions.” Journal of Cleaner Production 210 ( February). Elsevier Ltd: 1343–1365. doi:10.1016/j.jclepro.2018.11.025.
  • Rifkin, J. 2000. The Age of Access: The New Culture of Hypercapitalism, Where All of Life Is a Paid-For Experience. New York: JP Tarcher/Putnam.
  • Rondini, A., F. Tornese, M. G. Gnoni, G. Pezzotta, and R. Pinto. 2017. “Hybrid Simulation Modelling as A Supporting Tool for Sustainable Product Service Systems: A Critical Analysis.” International Journal of Production Research 55 (23): 6932–6945. doi:10.1080/00207543.2017.1330569. Taylor and Francis Ltd.
  • Sina Tayarani-Bathaie, S., Z. N. Sadough Vanini, and K. Khorasani. February 2014. “Dynamic Neural Network-Based Fault Diagnosis of Gas Turbine Engines.” Neurocomputing 125: 153–165. Elsevier. 10.1016/j.neucom.2012.06.050.
  • Spacagna, G. 2018. “Deep Time-to-Failure: Survival Analysis and Time-to-Failure Predictive Modeling Using Weibull Distributions and Recurrent Neural Networks in Keras.”
  • Sutton, R. S., and A. G. Barto. 2018. Reinforcement Learning, Second Edition: An Introduction. Cambridge, Massachusetts: MIT press.
  • Talhi, A., V. Fortineau, J. C. Huet, and S. Lamouri. 2019. “Ontology for Cloud Manufacturing Based Product Lifecycle Management.” Journal of Intelligent Manufacturing 30 (5): 2171–2192. doi:10.1007/s10845-017-1376-5. Springer New York LLC.
  • Tao, F., J. Cheng, Q. Qi, M. Zhang, H. Zhang, and F. Sui. 2018. “Digital Twin-Driven Product Design, Manufacturing and Service with Big Data.” The International Journal of Advanced Manufacturing Technology 94: 3563–3576. doi:10.1007/s00170-017-0233-1.
  • Wang, L., Z. Zhang, H. Long, X. Jia, and R. Liu. 2017. “Wind Turbine Gearbox Failure Identification with Deep Neural Networks.“ IEEE Transactions on Industrial Informatics 13 (3): 1360–1368. doi:10.1109/TII.2016.2607179.
  • Wang, N., S. Ren, Y. Liu, M. Yang, J. Wang, and D. Huisingh. August 2020. “An Active Preventive Maintenance Approach of Complex Equipment Based on a Novel Product-Service System Operation Mode.” Journal of Cleaner Production 277: 123365. Elsevier BV. 10.1016/j.jclepro.2020.123365.
  • Wingfield, N. 2017. “Automakers Race to Get Ahead of Silicon Valley on Car-Sharing.” New York Times.
  • Woyke, E. 2017. “General Electric Builds an AI Workforce.” MIT Technology Review 1: 1–3.
  • Yao, L., Q. Dong, J. Jiang, and F. Ni. 2020. “Deep Reinforcement Learning for Long‐term Pavement Maintenance Planning.” Computer-Aided Civil and Infrastructure Engineering 35 (11): 1230–1245. doi:10.1111/mice.12558. Blackwell Publishing Inc.
  • Yousefi, N., S. Tsianikas, and D. W. Coit. 2020. “Reinforcement Learning for Dynamic Condition-Based Maintenance of a System with Individually Repairable Components.” Quality Engineering 32 (3): 388–408. doi:10.1080/08982112.2020.1766692. Taylor and Francis Inc.
  • Zhang, Y., S. Ren, Y. Liu, and S. Si. January 2017b. “A Big Data Analytics Architecture for Cleaner Manufacturing and Maintenance Processes of Complex Products.” Journal of Cleaner Production 142: 626–641. Elsevier Ltd. 10.1016/j.jclepro.2016.07.123.
  • Zhang, Y., S. Ren, Y. Liu, T. Sakao, and D. Huisingh. August 2017a. “A Framework for Big Data Driven Product Lifecycle Management.” Journal of Cleaner Production 159: 229–240. Elsevier Ltd. 10.1016/j.jclepro.2017.04.172.
  • Zhang, Y., Z. Guo, L. Jingxiang, and Y. Liu. 2018. “A Framework for Smart Production-Logistics Systems Based on CPS and Industrial IoT.“ IEEE Transactions on Industrial Informatics 14 (9): 4019–4032. doi:10.1109/TII.2018.2845683.
  • Zhao, Z. Q., P. Zheng, X. Shou Tao, and W. Xindong 2019. “Object Detection with Deep Learning: A Review.“ IEEE Transactions on Neural Networks and Learning Systems 30 (11): 3212–3232. doi:10.1109/TNNLS.2018.2876865.