Advancing Anaesthetist Rostering Quality: A Practical Approach Toward Fairness and Efficiency
The Anaesthetist Rostering Problem (ARP) presents significant challenges in healthcare management due to complex constraints and regulations. Existing models for the ARP often fail to address the complexities of real-world hospital environments, particularly in integrating monthly and weekly schedul...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10830487/ |
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| author | Norizal Abdullah Masri Ayob Meng Chun Lam Nasser R. Sabar Graham Kendall Liu Chian Yong |
| author_facet | Norizal Abdullah Masri Ayob Meng Chun Lam Nasser R. Sabar Graham Kendall Liu Chian Yong |
| author_sort | Norizal Abdullah |
| collection | DOAJ |
| description | The Anaesthetist Rostering Problem (ARP) presents significant challenges in healthcare management due to complex constraints and regulations. Existing models for the ARP often fail to address the complexities of real-world hospital environments, particularly in integrating monthly and weekly schedules across multiple locations. This study addresses the key question: How can we develop anaesthetist rosters that improve both staff fairness and operational efficiency while meeting complex hospital requirements? To address this challenge, we propose a novel mixed-integer linear programming model with updated constraints, parameters, and an enhanced evaluation function. We implemented the model at Hospital Canselor Tuanku Muhriz (HCTM), Malaysia, to handle various shift types across multiple locations for both monthly and weekly rosters. The proposed model optimises the roster by satisfying mandatory constraints first (such as legal requirements), and then minimising soft constraint violations, such as employee preferences, through iterative refinement. The evaluation function assesses roster quality by minimising penalties for soft constraint violations. We modified the evaluation function and constraints to accommodate HCTM’s specific shift patterns and rest requirements, enhancing fairness, flexibility, and workload distribution. The model reduced penalties by 69.57% for monthly rosters and 64.37% for weekly rosters compared to manual scheduling. Statistical analysis proved significant enhancement in monthly rosters and weekly rosters. The model improves workload fairness and scheduling efficiency, bridging theoretical models and practical applications. It contributes to healthcare workforce management by offering better resource allocation and increased staff satisfaction. |
| format | Article |
| id | doaj-art-03227c330c4b4bfdabbb61391309543b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-03227c330c4b4bfdabbb61391309543b2025-01-25T00:02:34ZengIEEEIEEE Access2169-35362025-01-0113126921270810.1109/ACCESS.2025.352684210830487Advancing Anaesthetist Rostering Quality: A Practical Approach Toward Fairness and EfficiencyNorizal Abdullah0https://orcid.org/0000-0002-0161-7649Masri Ayob1https://orcid.org/0000-0002-5157-7921Meng Chun Lam2https://orcid.org/0000-0002-9435-9473Nasser R. Sabar3https://orcid.org/0000-0002-0276-4704Graham Kendall4https://orcid.org/0000-0003-2006-5103Liu Chian Yong5https://orcid.org/0000-0002-2663-6782Data Mining and Optimization Lab, Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, MalaysiaData Mining and Optimization Lab, Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, MalaysiaMixed Reality and Pervasive Computing Lab, Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, MalaysiaDepartment of Computer Science and Information Technology, La Trobe University, Melbourne, Bundoora, VIC, AustraliaSchool of Engineering and Computing, MILA University, Nilai, Negeri Sembilan, MalaysiaDepartment of Anaesthesiology and Intensive Care, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, MalaysiaThe Anaesthetist Rostering Problem (ARP) presents significant challenges in healthcare management due to complex constraints and regulations. Existing models for the ARP often fail to address the complexities of real-world hospital environments, particularly in integrating monthly and weekly schedules across multiple locations. This study addresses the key question: How can we develop anaesthetist rosters that improve both staff fairness and operational efficiency while meeting complex hospital requirements? To address this challenge, we propose a novel mixed-integer linear programming model with updated constraints, parameters, and an enhanced evaluation function. We implemented the model at Hospital Canselor Tuanku Muhriz (HCTM), Malaysia, to handle various shift types across multiple locations for both monthly and weekly rosters. The proposed model optimises the roster by satisfying mandatory constraints first (such as legal requirements), and then minimising soft constraint violations, such as employee preferences, through iterative refinement. The evaluation function assesses roster quality by minimising penalties for soft constraint violations. We modified the evaluation function and constraints to accommodate HCTM’s specific shift patterns and rest requirements, enhancing fairness, flexibility, and workload distribution. The model reduced penalties by 69.57% for monthly rosters and 64.37% for weekly rosters compared to manual scheduling. Statistical analysis proved significant enhancement in monthly rosters and weekly rosters. The model improves workload fairness and scheduling efficiency, bridging theoretical models and practical applications. It contributes to healthcare workforce management by offering better resource allocation and increased staff satisfaction.https://ieeexplore.ieee.org/document/10830487/Schedulinganaesthetist rostering problem (ARP)mathematical modelpersonnel rostering |
| spellingShingle | Norizal Abdullah Masri Ayob Meng Chun Lam Nasser R. Sabar Graham Kendall Liu Chian Yong Advancing Anaesthetist Rostering Quality: A Practical Approach Toward Fairness and Efficiency IEEE Access Scheduling anaesthetist rostering problem (ARP) mathematical model personnel rostering |
| title | Advancing Anaesthetist Rostering Quality: A Practical Approach Toward Fairness and Efficiency |
| title_full | Advancing Anaesthetist Rostering Quality: A Practical Approach Toward Fairness and Efficiency |
| title_fullStr | Advancing Anaesthetist Rostering Quality: A Practical Approach Toward Fairness and Efficiency |
| title_full_unstemmed | Advancing Anaesthetist Rostering Quality: A Practical Approach Toward Fairness and Efficiency |
| title_short | Advancing Anaesthetist Rostering Quality: A Practical Approach Toward Fairness and Efficiency |
| title_sort | advancing anaesthetist rostering quality a practical approach toward fairness and efficiency |
| topic | Scheduling anaesthetist rostering problem (ARP) mathematical model personnel rostering |
| url | https://ieeexplore.ieee.org/document/10830487/ |
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