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However, prolonged waiting for IVF procedures has become a problem of great concern, since this technology is only mastered by the large general hospitals. To deal with the insufficiency of IVF service capacity, this paper studies an IVF queuing network in an integrated cloud healthcare system, where the two key medical services, that is, egg retrieval and transplantation, are assigned to accomplish in the general hospital, while the routine medical tests are assigned into the community hospital. Based on continuous\u2010time Markov procedure, a dynamic large\u2010scale server scheduling problem in this complicated service network is modeled with consideration of different arrival rates of multiple type of patients and different service capacities of multiple servers that can be defined as doctors of the general hospital. To solve this model, a reinforcement learning (RL) algorithm is proposed, where the reward functions are designed for four conflicting subcosts: setup cost, patient waiting cost, penalty cost for unsatisfied patient personal preferences, and medical cost of patient. The experimental results show that the optimal service rule of each server\u2019s queue obtained by the RL method is significantly superior to the traditional service rule.<\/jats:p>","DOI":"10.1155\/2021\/6670288","type":"journal-article","created":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T20:20:06Z","timestamp":1611260406000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dynamic Large\u2010Scale Server Scheduling for IVF Queuing Network in Cloud Healthcare System"],"prefix":"10.1155","volume":"2021","author":[{"given":"Yafei","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8954-0876","authenticated-orcid":false,"given":"Hongfeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8645-6647","authenticated-orcid":false,"given":"Yaping","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,1,21]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1093\/humupd\/dms061"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/jsyst.2020.3014093"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cirp.2011.03.041"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10951-008-0090-8"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2017.12.021"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.06.044"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2020.05.012"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1080\/17517575.2019.1597169"},{"key":"e_1_2_10_9_2","first-page":"591","article-title":"Allocation of intensive care unit beds in periods of high demand","volume":"68","author":"Ouyang H.","year":"2020","journal-title":"Operations Research"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1080\/07408170304395"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2015.1389"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1080\/0740817x.2015.1011355"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2018.3145"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2018.02.001"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2018.1816"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1137\/130926705"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2016.2704"},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.omega.2018.06.009"},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2011.09.007"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2014.2067"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1287\/stsy.2018.0020"},{"key":"e_1_2_10_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-5885-2_16"},{"key":"e_1_2_10_23_2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2017.2905"},{"volume-title":"The Process Implications of Using Telemedicine for Chronically Ill Patients: Analyzing Key Consequences for Patients and Medical Sspecialists","year":"2015","author":"Rajan B.","key":"e_1_2_10_24_2"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1099-0526(199811\/12)4:2<49::AID-CPLX10>3.0.CO;2-V"},{"key":"e_1_2_10_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-016-9471-0"},{"key":"e_1_2_10_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2015.2474153"},{"key":"e_1_2_10_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105726"},{"key":"e_1_2_10_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.05.034"},{"key":"e_1_2_10_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2010.09.002"},{"key":"e_1_2_10_31_2","unstructured":"NoureddineD. 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