Two-Stage Predictive Maintenance Planning for Hospital Buildings: A Multiple-Objective Optimization-Based Clustering Approach
Publication: Journal of Performance of Constructed Facilities
Volume 36, Issue 1
Abstract
Prioritization of maintenance and rehabilitation interventions according to their urgency, expected improvements, costs, and downtimes is a necessary task in facility management. This is particularly of interest in critical facilities requiring nondisrupted operations or minimal stoppages such as hospital buildings. Maintenance scheduling is currently performed in hospitals in a purely subjective manner where experts categorize the importance and priority levels of different interventions and plan them according to their associated cost. Despite its popularity and workability, this process can be criticized for its high dependence on the expert’s knowledge and experience as well as its sole dependence on cost as the main driver for applying interventions. Therefore, in this paper, an objective methodology is developed to select the most suitable interventions and accordingly group the different actions together. This is expected to minimize the overall downtime and disruption caused by the maintenance and rehabilitation works in healthcare facilities. Introducing a combined artificial intelligence-based methodology, a triobjective optimization model is primarily utilized to select the interventions expected to maximize the overall performance of the facility as well as inhibit the minimum levels of costs and downtime. The output is further fed into an unsupervised machine learning model where interventions are grouped together according to their relevancy on a hybrid clustering approach, integrating between hierarchical and -means clustering algorithms. This yields an action plan for use by maintenance personnel to schedule the disruptions of critical spaces (i.e., intensive care units and operation rooms) within the hospital building. This helps ensure a smooth and continuous operation in the facility as well as prevent any harm to facility occupants that could result from maintenance and rehabilitation works.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Ahmed, R., F. Nasiri, and T. Zayed. 2020a. “Neutrosophic-AHP-based GA model for renewals planning of hospital building assets.” In Proc., 2020 Int. Conf. on Decision Aid Sciences and Applications (DASA’20). New York: IEEE. https://doi.org/10.1109/DASA51403.2020.9317119.
Ahmed, R., F. Nasiri, and T. Zayed. 2020b. “Towards efficient hospital maintenance scheduling: A neutrosophic-based decision tree prioritization framework.” In Proc., 2020 INFORMS Conf. on Service Science. State College, PA: Penn State Center for Health Organization Transformation.
Ahmed, R., F. Nasiri, and T. Zayed. 2021. “A novel neutrosophic-based machine learning approach for maintenance prioritization in healthcare facilities.” J. Build. Eng. 42 (Oct): 102480. https://doi.org/10.1016/j.jobe.2021.102480.
Ahmed, R., and T. Zayed. 2019. “Criticality assessment of hospital building systems.” In Proc., CSCE Annual Conf.: Growing with Youth—Croître avec les Jeunes, Laval (Greater Montreal). Whistler, BC, Canada: Canadian Society for Civil Engineering.
Ahmed, R., T. Zayed, and F. Nasiri. 2020c. “A hybrid genetic algorithm-based fuzzy Markovian model for the deterioration modeling of healthcare facilities.” Algorithms 13 (9): 210. https://doi.org/10.3390/a13090210.
Binnenmars, N. 2018. “Implementing dynamic clustering of maintenance activities at AIS Airlines.” M.Sc. thesis, Faculty of Behavioural Management and Social Sciences, Dept. of Industrial Engineering and Business Information Systems, Univ. of Twente.
Brauers, W. K. 2004. Optimization methods for a stakeholder society: A revolution in economic thinking by multi-objective optimization. New York: Springer. https://doi.org/10.1007/978-1-4419-9178-2.
Chen, T., and S. Chien. 2011. “Flexible hardware architecture of hierarchical K-means clustering for large cluster number.” IEEE Trans. Very Large Scale Integr. Syst. 19 (8): 1336–1345. https://doi.org/10.1109/TVLSI.2010.2049669.
Cheng, S., Y. Shi, and Q. Qin. 2012. “On the performance metrics of multiobjective optimization.” In Advances in swarm intelligence, edited by Y. Tan, Y. Shi, and Z. Ji, 504–512. Berlin: Springer.
Chin, H. H., P. S. Varbanov, J. J. Klemeš, M. F. D. Benjamin, and R. R. Tan. 2020. “Asset maintenance optimisation approaches in the chemical and process industries—A review.” Chem. Eng. Res. Des. 164 (Dec): 162–194. https://doi.org/10.1016/j.cherd.2020.09.034.
de Almeida A. T., C. A. V. Cavalcante, M. H. Alencar, R. J. P. Ferreira, A. T. de Almeida-Filho, and T. V. Garcez. 2015. Multicriteria and multiobjective models for risk, reliability and maintenance decision analysis. New York: Springer. https://doi.org/10.1007/978-3-319-17969-8.
Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE Trans. Evol. Comput. 6 (2): 182–197. https://doi.org/10.1109/4235.996017.
Diakoulaki, D., G. Mavrotas, and L. Papayannakis. 1995. “Determining objective weights in multiple criteria problems: The critic method.” Comput. Oper. Res. 22 (7): 763–770. https://doi.org/10.1016/0305-0548(94)00059-H.
Hassanat, A., K. Almohammadi, E. Alkafaween, E. Abunawas, A. Hammouri, and V. B. S. Prasath. 2019. “Choosing mutation and crossover ratios for genetic algorithms—A review with a new dynamic approach.” Information 10 (12): 390. https://doi.org/10.3390/info10120390.
Koksal, A., A. Ozdemir, and O. Ata. 2017. “RCAM based maintenance plan of the power transformers using k-means clustering algorithm.” In Proc., 19th Int. Conf. on Intelligent System Application to Power Systems (ISAP), 1–6. New York: IEEE. https://doi.org/10.1109/ISAP.2017.8071391.
Li, H., H. Zuo, K. Liang, J. Xu, J. Cai, and J. Liu. 2016. “Optimizing combination of aircraft maintenance tasks by adaptive genetic algorithm based on cluster search.” J. Syst. Eng. Electron. 27 (1): 140–156.
Liu, J., X. Wei, J. Ye, B. Zeng, and M. Li. 2020. “Preventive group maintenance strategy for in-service agricultural machinery equipment.” Trans. Chin. Soc. Agric. Mach. 51: 316–322. https://doi.org/10.6041/j.issn.1000-1298.2020.S2.037.
Mahfoud, H., A. El Barkany, and A. El Biyaali. 2016. “Preventive maintenance optimization in healthcare domain: Status of research and perspective.” J. Qual. Reliab. Eng. 2016: 1–10. https://doi.org/10.1155/2016/5314312.
Mohan Kumar, K. N., S. Sampath, M. Imran, and N. Pradeep. 2021. “Clustering diagnostic codes: Exploratory machine learning approach for preventive care of chronic diseases.” In Intelligent data engineering and analytics, edited by S. C. Satapathy, Y.-D. Zhang, V. Bhateja, and R. Majhi, 551–564. New York: Springer. https://doi.org/10.1007/978-981-15-5679-1_53.
Monsef, H., M. Naghashzadegan, A. Jamali, and R. Farmani. 2019. “Comparison of evolutionary multi objective optimization algorithms in optimum design of water distribution network.” Ain Shams Eng. J. 10 (1): 103–111. https://doi.org/10.1016/j.asej.2018.04.003.
Olanrewaju, A., W. W. Fang, and S. Y. Tan. 2018. “Hospital building maintenance management model.” Int. J. Eng. Technol. 7 (2.29): 747–753. https://doi.org/10.14419/ijet.v7i2.29.14010.
Oteyaka, H. C. 2008. “Optimal cost and availability replacement models for multi-component systems.” Theses and dissertations, Département de génie mécanique, Faculté des sciences et de génie, Université Laval.
RSMeans. 2021. Facilities maintenance and repair costs with RSMeans data. 28th ed. Rockland, MA: RSMeans Data.
Salem, D., and E. Elwakil. 2018. “Develop an assessment model for healthcare facilities: A framework to prioritize the asset criticality for the capital renewals.” In Proc., Int. Conf. on Construction and Real Estate Management 2018, 82–88. Reston, VA: ASCE. https://doi.org/10.1061/9780784481752.011.
Salem, D., and E. Elwakil. 2020. “Asset condition assessment model for healthcare facilities.” Int. J. Construct. Manage. 1–38. https://doi.org/10.1080/15623599.2020.1857002.
Seif, Z., E. Mardaneh, R. Loxton, and A. Lockwood. 2021. “Minimizing equipment shutdowns in oil and gas campaign maintenance.” J. Oper. Res. Soc. 72 (7): 1486–1504. https://doi.org/10.1080/01605682.2020.1745699.
Sujan, M. A., I. Habli, T. P. Kelly, S. Pozzi, and C. W. Johnson. 2016. “Should healthcare providers do safety cases? Lessons from a cross-industry review of safety case practices.” Saf. Sci. 84 (Apr): 181–189. https://doi.org/10.1016/j.ssci.2015.12.021.
Tan, K. C., T. H. Lee, and E. F. Khor. 2001. “Evolutionary algorithms for multi-objective optimization: Performance assessments and comparisons.” In Vol. 2 of Proc., 2001 Congress on Evolutionary Computation, 979–986. New York: IEEE. https://doi.org/10.1109/CEC.2001.934296.
UN (United Nations). 2015. Transforming our world: The 2030 agenda for sustainable development (A/RES/70/1). Berlin: Springer. https://doi.org/10.1891/9780826190123.ap02.
Vala, S., P. Chemweno, L. Pintelon, and P. Muchiri. 2018. “A risk-based maintenance approach for critical care medical devices: A case study application for a large hospital in a developing country.” Int. J. Syst. Assur. Eng. Manage. 9 (5): 1217–1233. https://doi.org/10.1007/s13198-018-0705-1.
Wu, B. R. 2013. “Condition based maintenance optimization using data driven methods.” Ph.D. thesis, Dept. of Mechanical and Industrial EngineeringConcordia Univ.
Xiao, P., H. Ju, Q. Li, H. Xu, and C. Lu. 2020. “Task planning of space maintenance robot using modified clustering method.” IEEE Access 8: 45618–45626. https://doi.org/10.1109/ACCESS.2020.2978122.
Yen, G. G., and Z. He. 2014. “Performance metric ensemble for multiobjective evolutionary algorithms.” IEEE Trans. Evol. Comput. 18 (1): 131–144. https://doi.org/10.1109/TEVC.2013.2240687.
Yousefli, Z., F. Nasiri, and O. Moselhi. 2017. “Healthcare facilities maintenance management: A literature review.” J. Facil. Manage. 15 (4): 352–375. https://doi.org/10.1108/JFM-10-2016-0040.
Zhang, J., and D. Chang. 2019. “Semi-supervised patient similarity clustering algorithm based on electronic medical records.” IEEE Access 7: 90705–90714. https://doi.org/10.1109/ACCESS.2019.2923333.
Zhang, Q. 2013. “Case study of cost benefits of condition based maintenance used in medical devices.” In Proc., Annual Reliability and Maintainability Symp., 1–5. New York: IEEE. https://doi.org/10.1109/RAMS.2013.6517676.
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Published In
Journal of Performance of Constructed Facilities
Volume 36 • Issue 1 • February 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 25, 2021
Accepted: Sep 28, 2021
Published online: Oct 29, 2021
Published in print: Feb 1, 2022
Discussion open until: Mar 29, 2022
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- Reem Ahmed, Fuzhan Nasiri, Tarek Zayed, Genetic Algorithm-based Clustering Methodology for Maintenance Scheduling in Healthcare Facilities, 2021 International Conference on Decision Aid Sciences and Application (DASA), 10.1109/DASA53625.2021.9682280, (643-646), (2021).