{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:54Z","timestamp":1755219834712,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Hospital Sterilization and Decontamination Units (HSDUs) play a critical role in ensuring the safe and efficient decontamination of surgical instruments to prevent healthcare-associated infections. This paper presents a mathematical modeling approach using Discrete Event Simulation (DES) to optimize staffing schedules within HSDUs at a district general hospital using Metaheuristic Optimization. The objective is to identify the optimal number of staff required to meet service demands while maintaining operational efficiency. A Greedy Heuristic and Tabu Search are employed to determine staffing levels that meet specific performance targets, including decontaminating at least 99% of items within the target time. Results showed that the Tabu Search algorithm significantly outperforms the Greedy Heuristic while maintaining 99% of items decontaminated within the required time. Our findings suggest that Tabu Search provides a more reliable and efficient method for optimizing staffing in HSDUs. Further work is recommended to explore cost implications of staffing decisions.<\/jats:p>","DOI":"10.3233\/shti250966","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:36:39Z","timestamp":1754566599000},"source":"Crossref","is-referenced-by-count":0,"title":["Metaheuristic Optimization for Hospital Sterilisation and Decontamination Services"],"prefix":"10.3233","author":[{"given":"Thomas","family":"Davies","sequence":"first","affiliation":[{"name":"Cardiff University, School of Mathematics, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7565-4189","authenticated-orcid":false,"given":"Tracey J.","family":"England","sequence":"additional","affiliation":[{"name":"Southampton University, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5772-5307","authenticated-orcid":false,"given":"Doris A.","family":"Behrens","sequence":"additional","affiliation":[{"name":"University of Krems, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4361-8559","authenticated-orcid":false,"given":"Daniel","family":"Gartner","sequence":"additional","affiliation":[{"name":"Southampton University, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250966","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:36:39Z","timestamp":1754566599000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250966"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250966","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}