An intelligent optimization strategy for medical doctor rostering using hybrid genetic algorithm-particle swarm optimization in Malaysian public hospital
Comparing manual rostering to automated rostering reveals that manual rostering is typically more challenging, time-consuming, and exhausting for doctors, particularly due to shifting business regulations, a shortage of healthcare professionals, and heavy workloads. During rostering, it is essential...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTM Press
2025
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/44033/ http://umpir.ump.edu.my/id/eprint/44033/1/An%20intelligent%20optimization%20strategy%20for%20medical%20doctor.pdf |
| _version_ | 1848827015246905344 |
|---|---|
| author | Zanariah, Zainudin Shafaatunnur, Hasan Nurfazrina, Mohd Zamry Nor‘Afifah, Sabri Nurul Syafidah, Jamil Norliana, Muslim Nur Amalina, Mat Jan Noraini, Ibrahim |
| author_facet | Zanariah, Zainudin Shafaatunnur, Hasan Nurfazrina, Mohd Zamry Nor‘Afifah, Sabri Nurul Syafidah, Jamil Norliana, Muslim Nur Amalina, Mat Jan Noraini, Ibrahim |
| author_sort | Zanariah, Zainudin |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Comparing manual rostering to automated rostering reveals that manual rostering is typically more challenging, time-consuming, and exhausting for doctors, particularly due to shifting business regulations, a shortage of healthcare professionals, and heavy workloads. During rostering, it is essential to consider both hard and soft constraints to minimize constraint violations, maximize medical doctor satisfaction, and meet all requirements for hard constraints. To address these challenges, this paper proposes Hybrid Genetic Algorithm and Particle Swarm Optimization (Hybrid GA-PSO) to model rostering. In this approach, one set population of working days represents the rostering structure, which is determined using evolutionary-inspired operators, search, and update procedures. Additionally, the paper conducts observations and interviews with relevant personnel in a Malaysian hospital to gather insights and highlight constraints associated with medical doctors rostering. Rostering requirements determine the relative importance of the hard and soft constraints. The results of the research indicate that the Hybrid GA-PSO approach can produce workable rosters that reduce the workload of physicians and shorten the time needed to create rosters by the total violation of both soft and hard constraints and accuracy. It also ensures compliance with both hard and soft criteria and improves rostering accuracy. |
| first_indexed | 2025-11-15T03:53:59Z |
| format | Article |
| id | ump-44033 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:53:59Z |
| publishDate | 2025 |
| publisher | Penerbit UTM Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-440332025-03-11T05:00:13Z http://umpir.ump.edu.my/id/eprint/44033/ An intelligent optimization strategy for medical doctor rostering using hybrid genetic algorithm-particle swarm optimization in Malaysian public hospital Zanariah, Zainudin Shafaatunnur, Hasan Nurfazrina, Mohd Zamry Nor‘Afifah, Sabri Nurul Syafidah, Jamil Norliana, Muslim Nur Amalina, Mat Jan Noraini, Ibrahim QA Mathematics Comparing manual rostering to automated rostering reveals that manual rostering is typically more challenging, time-consuming, and exhausting for doctors, particularly due to shifting business regulations, a shortage of healthcare professionals, and heavy workloads. During rostering, it is essential to consider both hard and soft constraints to minimize constraint violations, maximize medical doctor satisfaction, and meet all requirements for hard constraints. To address these challenges, this paper proposes Hybrid Genetic Algorithm and Particle Swarm Optimization (Hybrid GA-PSO) to model rostering. In this approach, one set population of working days represents the rostering structure, which is determined using evolutionary-inspired operators, search, and update procedures. Additionally, the paper conducts observations and interviews with relevant personnel in a Malaysian hospital to gather insights and highlight constraints associated with medical doctors rostering. Rostering requirements determine the relative importance of the hard and soft constraints. The results of the research indicate that the Hybrid GA-PSO approach can produce workable rosters that reduce the workload of physicians and shorten the time needed to create rosters by the total violation of both soft and hard constraints and accuracy. It also ensures compliance with both hard and soft criteria and improves rostering accuracy. Penerbit UTM Press 2025-02-21 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/44033/1/An%20intelligent%20optimization%20strategy%20for%20medical%20doctor.pdf Zanariah, Zainudin and Shafaatunnur, Hasan and Nurfazrina, Mohd Zamry and Nor‘Afifah, Sabri and Nurul Syafidah, Jamil and Norliana, Muslim and Nur Amalina, Mat Jan and Noraini, Ibrahim (2025) An intelligent optimization strategy for medical doctor rostering using hybrid genetic algorithm-particle swarm optimization in Malaysian public hospital. Malaysian Journal of Fundamental and Applied Sciences, 21. pp. 1642-1653. ISSN 2289-599x. (Published) https://doi.org/10.11113/mjfas.v21n1.3572 https://doi.org/10.11113/mjfas.v21n1.3572 |
| spellingShingle | QA Mathematics Zanariah, Zainudin Shafaatunnur, Hasan Nurfazrina, Mohd Zamry Nor‘Afifah, Sabri Nurul Syafidah, Jamil Norliana, Muslim Nur Amalina, Mat Jan Noraini, Ibrahim An intelligent optimization strategy for medical doctor rostering using hybrid genetic algorithm-particle swarm optimization in Malaysian public hospital |
| title | An intelligent optimization strategy for medical doctor rostering using hybrid genetic algorithm-particle swarm optimization in Malaysian public hospital |
| title_full | An intelligent optimization strategy for medical doctor rostering using hybrid genetic algorithm-particle swarm optimization in Malaysian public hospital |
| title_fullStr | An intelligent optimization strategy for medical doctor rostering using hybrid genetic algorithm-particle swarm optimization in Malaysian public hospital |
| title_full_unstemmed | An intelligent optimization strategy for medical doctor rostering using hybrid genetic algorithm-particle swarm optimization in Malaysian public hospital |
| title_short | An intelligent optimization strategy for medical doctor rostering using hybrid genetic algorithm-particle swarm optimization in Malaysian public hospital |
| title_sort | intelligent optimization strategy for medical doctor rostering using hybrid genetic algorithm-particle swarm optimization in malaysian public hospital |
| topic | QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/44033/ http://umpir.ump.edu.my/id/eprint/44033/ http://umpir.ump.edu.my/id/eprint/44033/ http://umpir.ump.edu.my/id/eprint/44033/1/An%20intelligent%20optimization%20strategy%20for%20medical%20doctor.pdf |