Multi-resources co-scheduling optimization for home healthcare services under the constraints of service time windows and green transportation
Resource sharing among different communities and medical centers is an effective method to solve the inefficiency of healthcare resource use and unequal distribution of medical resources across regions. This study aims at optimizing the resource arrangement in home health care services by multi-reso...
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| Veröffentlicht in: | Applied soft computing Jg. 131; S. 109746 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.12.2022
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | Resource sharing among different communities and medical centers is an effective method to solve the inefficiency of healthcare resource use and unequal distribution of medical resources across regions. This study aims at optimizing the resource arrangement in home health care services by multi-resources co-scheduling. Specifically, we consider several important constraints including cross-distinct income in the constructed model. A Multi-Regions Tabu Search Algorithm is proposed to realize co-scheduling of multi-medical-staff in different communities to improve the calculation speed and obtain a better solution. We take the PuTuo district in Shanghai as an example for empirical analysis to verify the effectiveness of the proposed model and algorithm. Sensitivity analysis is then conducted by changing the service capabilities of communities. Results indicate that the total cost can be effectively reduced by multi-resources co-scheduling under different service capacities. However, the levels of cost reduction are distinct in different situations.
•We optimize resource arrangement in home healthcare service by multi-resources co-scheduling.•We propose a Multi-Regions-Tabu Search Algorithm to realize co-scheduling.•The empirical analysis verified the effectiveness of the proposed model and algorithm.•The total cost can be effectively reduced by multi-resources co-scheduling. |
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| AbstractList | Resource sharing among different communities and medical centers is an effective method to solve the inefficiency of healthcare resource use and unequal distribution of medical resources across regions. This study aims at optimizing the resource arrangement in home health care services by multi-resources co-scheduling. Specifically, we consider several important constraints including cross-distinct income in the constructed model. A Multi-Regions Tabu Search Algorithm is proposed to realize co-scheduling of multi-medical-staff in different communities to improve the calculation speed and obtain a better solution. We take the PuTuo district in Shanghai as an example for empirical analysis to verify the effectiveness of the proposed model and algorithm. Sensitivity analysis is then conducted by changing the service capabilities of communities. Results indicate that the total cost can be effectively reduced by multi-resources co-scheduling under different service capacities. However, the levels of cost reduction are distinct in different situations.
•We optimize resource arrangement in home healthcare service by multi-resources co-scheduling.•We propose a Multi-Regions-Tabu Search Algorithm to realize co-scheduling.•The empirical analysis verified the effectiveness of the proposed model and algorithm.•The total cost can be effectively reduced by multi-resources co-scheduling. |
| ArticleNumber | 109746 |
| Author | Tian, Yao Ouyang, Xiaoling Du, Gang |
| Author_xml | – sequence: 1 givenname: Gang surname: Du fullname: Du, Gang organization: School of Business and Administration, Faculty of Economics and Management, East China Normal University, Shanghai 200062, China – sequence: 2 givenname: Yao surname: Tian fullname: Tian, Yao organization: School of Business and Administration, Faculty of Economics and Management, East China Normal University, Shanghai 200062, China – sequence: 3 givenname: Xiaoling surname: Ouyang fullname: Ouyang, Xiaoling email: xlouyang@foxmail.com organization: School of Economics, Faculty of Economics and Management, East China Normal University, Shanghai 200062, China |
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