{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T22:45:08Z","timestamp":1790289908299,"version":"4.1.0"},"reference-count":39,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,8,29]],"date-time":"2020-08-29T00:00:00Z","timestamp":1598659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Healthcare facilities are constantly deteriorating due to tight budgets allocated to the upkeep of building assets. This entails the need for improved deterioration modeling of such buildings in order to enforce a predictive maintenance approach that decreases the unexpected occurrence of failures and the corresponding downtime elapsed to repair or replace the faulty asset components. Currently, hospitals utilize subjective deterioration prediction methodologies that mostly rely on age as the sole indicator of degradation to forecast the useful lives of the building components. Thus, this paper aims at formulating a more efficient stochastic deterioration prediction model that integrates the latest observed condition into the forecasting procedure to overcome the subjectivity and uncertainties associated with the currently employed methods. This is achieved by means of developing a hybrid genetic algorithm-based fuzzy Markovian model that simulates the deterioration process given the scarcity of available data demonstrating the condition assessment and evaluation for such critical facilities. A nonhomogeneous transition probability matrix (TPM) based on fuzzy membership functions representing the condition, age and relative deterioration rate of the hospital systems is utilized to address the inherited uncertainties. The TPM is further calibrated by means of a genetic algorithm to circumvent the drawbacks of the expert-based models. A sensitivity analysis was carried out to analyze the possible changes in the output resulting from predefined modifications to the input parameters in order to ensure the robustness of the model. The performance of the deterioration prediction model developed is then validated through a comparison with a state-of-art stochastic model in contrast to real hospital datasets, and the results obtained from the developed model significantly outperformed the long-established Weibull distribution-based deterioration prediction methodology with mean absolute errors of 1.405 and 9.852, respectively. Therefore, the developed model is expected to assist decision-makers in creating more efficient maintenance programs as well as more data-driven capital renewal plans.<\/jats:p>","DOI":"10.3390\/a13090210","type":"journal-article","created":{"date-parts":[[2020,8,30]],"date-time":"2020-08-30T06:06:22Z","timestamp":1598767582000},"page":"210","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Hybrid Genetic Algorithm-Based Fuzzy Markovian Model for the Deterioration Modeling of Healthcare Facilities"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7955-7263","authenticated-orcid":false,"given":"Reem","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, QC H3G 1M8, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarek","family":"Zayed","sequence":"additional","affiliation":[{"name":"Department of Building and Real Estate, the Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuzhan","family":"Nasiri","sequence":"additional","affiliation":[{"name":"Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, QC H3G 1M8, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1108\/02632770410547570","article-title":"Healthcare facilities management: State of the art review","volume":"22","author":"Shohet","year":"2004","journal-title":"Facilities"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"012055","DOI":"10.1088\/1755-1315\/296\/1\/012055","article-title":"Industry 4.0 for failure information management within Proactive Maintenance","volume":"296","author":"Talamo","year":"2019","journal-title":"IOP Conf. 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