乐胖代购免代理版

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These systems aim to optimize the balance between online diagnosis and offline treatment to effectively reduce patients\u2019 waiting times and improve the utilization of idle medical resources. In this paper, a distributed genetic algorithm (DGA) is proposed as a means to optimize the balance of patient assignment (PA) in cloud healthcare systems. The proposed DGA utilizes individuals as solutions for the PA optimization problem and generates better solutions through the execution of crossover, mutation, and selection operators. Besides, the distributed framework in the DGA is proposed to improve its population diversity and scalability. Experimental results demonstrate the effectiveness of the proposed DGA in optimizing the PA problem within the cloud healthcare systems.<\/jats:p>","DOI":"10.1007\/s13755-023-00230-1","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T03:32:46Z","timestamp":1688009566000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm"],"prefix":"10.1007","volume":"11","author":[{"given":"Xinyu","family":"Pang","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5955-6295","authenticated-orcid":false,"given":"Yong-Feng","family":"Ge","sequence":"additional","affiliation":[]},{"given":"Kate","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Agma J. 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