Dynamic Large-Scale Server Scheduling for IVF Queuing Network in Cloud Healthcare System
As one of the most effective medical technologies for the infertile patients, in vitro fertilization (IVF) has been more and more widely developed in recent years. However, prolonged waiting for IVF procedures has become a problem of great concern, since this technology is only mastered by the large...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6670288 |
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| _version_ | 1849473056286703616 |
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| author | Yafei Li Hongfeng Wang Li Li Yaping Fu |
| author_facet | Yafei Li Hongfeng Wang Li Li Yaping Fu |
| author_sort | Yafei Li |
| collection | DOAJ |
| description | As one of the most effective medical technologies for the infertile patients, in vitro fertilization (IVF) has been more and more widely developed in recent years. 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-time Markov procedure, a dynamic large-scale 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’s queue obtained by the RL method is significantly superior to the traditional service rule. |
| format | Article |
| id | doaj-art-db88f24fad8e475d8051dcf1161d6992 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-db88f24fad8e475d8051dcf1161d69922025-08-20T03:24:20ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66702886670288Dynamic Large-Scale Server Scheduling for IVF Queuing Network in Cloud Healthcare SystemYafei Li0Hongfeng Wang1Li Li2Yaping Fu3College of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaXikang Healthcare Technology Co., Ltd, Shenyang, ChinaSchool of Business, Qingdao University, Qingdao, ChinaAs one of the most effective medical technologies for the infertile patients, in vitro fertilization (IVF) has been more and more widely developed in recent years. 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-time Markov procedure, a dynamic large-scale 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’s queue obtained by the RL method is significantly superior to the traditional service rule.http://dx.doi.org/10.1155/2021/6670288 |
| spellingShingle | Yafei Li Hongfeng Wang Li Li Yaping Fu Dynamic Large-Scale Server Scheduling for IVF Queuing Network in Cloud Healthcare System Complexity |
| title | Dynamic Large-Scale Server Scheduling for IVF Queuing Network in Cloud Healthcare System |
| title_full | Dynamic Large-Scale Server Scheduling for IVF Queuing Network in Cloud Healthcare System |
| title_fullStr | Dynamic Large-Scale Server Scheduling for IVF Queuing Network in Cloud Healthcare System |
| title_full_unstemmed | Dynamic Large-Scale Server Scheduling for IVF Queuing Network in Cloud Healthcare System |
| title_short | Dynamic Large-Scale Server Scheduling for IVF Queuing Network in Cloud Healthcare System |
| title_sort | dynamic large scale server scheduling for ivf queuing network in cloud healthcare system |
| url | http://dx.doi.org/10.1155/2021/6670288 |
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