International Journal of Computational Intelligence Systems

Volume 13, Issue 1, 2020, Pages 318 - 331

Applying Heuristic Algorithms to Solve Inter-hospital Hierarchical Allocation and Scheduling Problems of Medical Staff

Authors
Ping-Shun Chen1, Wen-Tso Huang2, 3, Tsung-Huan Chiang1, Gary Yu-Hsin Chen4, *
1 Department of Industrial and Systems Engineering, Chung Yuan Christian University, Chung Li District, Taoyuan County, 320, Taiwan, ROC
2 Business School, Minnan Normal University, No. 36, Qian Zhi Street, Xiangcheng District, Zhangzhou City, Fujian Province, China
3 Analysis and Application of Business Big Data in Key Laboratory of Universities in Fujian Province, No. 36, Qian Zhi Street, Xiangcheng District, Zhangzhou City, Fujian Province, China
4 Department of Logistics Management, National Kaohsiung University of Science & Technology, Yanchao District, Kaohsiung City, 824, Taiwan, ROC
*Corresponding author. Email: garychen@nkust.edu.tw
Corresponding Author
Gary Yu-Hsin Chen
Received 26 August 2019, Accepted 3 March 2020, Available Online 17 March 2020.
DOI
https://doi.org/10.2991/ijcis.d.200310.004How to use a DOI?
Keywords
Heuristic algorithms, Staff allocation, Staff scheduling, Inter-hospital, Particle swarm optimization
Abstract

To address the inter-hospital hierarchical allocation and scheduling problems, this research used the pooling resource concept to allocate medical staff among hospital branches as well as determine their monthly schedules. This study proposed a two-stage strategy. The first stage proposed three heuristic algorithms—HRA1 (human resource allocation based on the hospital's size), HRA2 (human resource allocation based on the average allocation), and HRA3 (human resource allocation based on the severity of penalties)—for medical staff allocation. The second stage used the improved particle swarm optimization (PSO) algorithm to schedule the medical staff within a reasonable time. Based on the numerical results, HRA3 was superior to HRA1 and HRA2. Furthermore, the analysis of two scenarios—varying the sizes of hospital branches (Scenario 1) and varying the total number of medical staff (Scenario 2)—showed that, when the sizes of hospital branches varied (Scenario 1), HRA3 was superior to HRA1 and HRA2 whereas, when the sizes were given (Scenario 2), the lowest number of medical staff possible was approximately 60. The findings of this research will help hospital managers make decisions concerning the allocation and scheduling of medical staff.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
318 - 331
Publication Date
2020/03
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.d.200310.004How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Ping-Shun Chen
AU  - Wen-Tso Huang
AU  - Tsung-Huan Chiang
AU  - Gary Yu-Hsin Chen
PY  - 2020
DA  - 2020/03
TI  - Applying Heuristic Algorithms to Solve Inter-hospital Hierarchical Allocation and Scheduling Problems of Medical Staff
JO  - International Journal of Computational Intelligence Systems
SP  - 318
EP  - 331
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200310.004
DO  - https://doi.org/10.2991/ijcis.d.200310.004
ID  - Chen2020
ER  -