{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T16:53:03Z","timestamp":1762102383082,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,13]],"date-time":"2019-05-13T00:00:00Z","timestamp":1557705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Military uniforms serve as an essential symbol for servicemen and an important image of national and military dignity. The current military uniform size system in Taiwan, which features various types of military uniforms based on the body sizes of servicemen, was formulated in 1986. This size classification system includes numerous groups and is too complex, leading to inventory overstock, increased inventory cost and warehouse staff workload, and a waste of national defense resources. This study used support vector clustering (SVC) with genetic algorithm (GA) models to improve the upper garment size system for uniforms. The SVC technique was employed to classify sizes, and the GA technique was used to determine optimal parameter values for the SVC model. This paper developed an upper garment size system that can increase the fit of uniforms to servicemen\u2019s body sizes and reduce the number of size groups, thereby alleviating warehouse staff workload and inventory cost.<\/jats:p>","DOI":"10.3390\/sym11050665","type":"journal-article","created":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T10:42:33Z","timestamp":1557830553000},"page":"665","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Development of a Military Uniform Size System Using Hybrid Support Vector Clustering with a Genetic Algorithm"],"prefix":"10.3390","volume":"11","author":[{"given":"Ting-Chen","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Health Business Administration, Hungkuang University, Taichung City 43302, Taiwan"}]},{"given":"Jason C.H.","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Business Administration, Gonzaga University, Spokane, WA 99258, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8500-9914","authenticated-orcid":false,"given":"Gino K.","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Management, Hungkuang University, Taichung City 43302, Taiwan"}]},{"given":"Cheng-Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Management, Hungkuang University, Taichung City 43302, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,13]]},"reference":[{"key":"ref_1","unstructured":"Burns, L.D., and Bryant, N.O. 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