Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future Directions
The Physician Scheduling Problem (PSP) has emerged as a critical challenge in healthcare management, directly relevant to Sustainable Development Goal 3 (SDG 3) - Good Health and Well-being. Driven by physician shortages, rising operational costs, and the need for efficient workforce planning, PSP a...
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2025-01-01
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| author | Norizal Abdullah Masri Ayob Meng Chun Lam Nasser R. Sabar Graham Kendall Mohamad Khairulamirin Md Razali |
| author_facet | Norizal Abdullah Masri Ayob Meng Chun Lam Nasser R. Sabar Graham Kendall Mohamad Khairulamirin Md Razali |
| author_sort | Norizal Abdullah |
| collection | DOAJ |
| description | The Physician Scheduling Problem (PSP) has emerged as a critical challenge in healthcare management, directly relevant to Sustainable Development Goal 3 (SDG 3) - Good Health and Well-being. Driven by physician shortages, rising operational costs, and the need for efficient workforce planning, PSP affects the quality of patient care, staff satisfaction, and the overall efficiency of the healthcare system. While previous reviews have addressed PSP, they are lacking in a comprehensive analysis of recent optimization methodologies and their effectiveness. This work aims to bridge this gap by analyzing 60 research studies which addressed PSP, published between January 2014 and June 2024. Our study also extends the problem definition, constraints, evaluation functions, and the variants of PSP. We examine a wide range of optimization methodologies, including mathematical programming, heuristics, matheuristics, and machine learning, highlighting their strengths and limitations in addressing the multifaceted nature of PSP. This review also analyzes the datasets used in PSP research, noting the lack of standardized benchmarks. Key findings reveal the prevalence of mathematical optimization methods, the growing importance of multi-objective optimization and robustness, as well as the potential of machine learning and data-driven approaches. Future research directions are outlined, emphasizing the need for more scalable algorithms, real-time scheduling capabilities, improved user interfaces, and comprehensive validation studies. This review contributes to the advancement of PSP optimization, aiming to enhance healthcare workforce management, improve patient care, and ultimately address the pressing challenges faced by healthcare systems worldwide, thus supporting the achievement of SDG 3 and promoting universal health coverage. |
| format | Article |
| id | doaj-art-2533615b74fe45b58ad0223675bc3108 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2533615b74fe45b58ad0223675bc31082025-01-10T00:01:25ZengIEEEIEEE Access2169-35362025-01-01135203521810.1109/ACCESS.2024.352459910819388Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future DirectionsNorizal 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/0000-0003-2006-5103Mohamad Khairulamirin Md Razali5https://orcid.org/0000-0003-4467-8641Data Mining and Optimization Laboratory, Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, MalaysiaData Mining and Optimization Laboratory, Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, MalaysiaMixed Reality and Pervasive Computing Laboratory, Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, MalaysiaDepartment of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, AustraliaSchool of Engineering and Computing, MILA University, Nilai, Negeri Sembilan, MalaysiaFaculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, MalaysiaThe Physician Scheduling Problem (PSP) has emerged as a critical challenge in healthcare management, directly relevant to Sustainable Development Goal 3 (SDG 3) - Good Health and Well-being. Driven by physician shortages, rising operational costs, and the need for efficient workforce planning, PSP affects the quality of patient care, staff satisfaction, and the overall efficiency of the healthcare system. While previous reviews have addressed PSP, they are lacking in a comprehensive analysis of recent optimization methodologies and their effectiveness. This work aims to bridge this gap by analyzing 60 research studies which addressed PSP, published between January 2014 and June 2024. Our study also extends the problem definition, constraints, evaluation functions, and the variants of PSP. We examine a wide range of optimization methodologies, including mathematical programming, heuristics, matheuristics, and machine learning, highlighting their strengths and limitations in addressing the multifaceted nature of PSP. This review also analyzes the datasets used in PSP research, noting the lack of standardized benchmarks. Key findings reveal the prevalence of mathematical optimization methods, the growing importance of multi-objective optimization and robustness, as well as the potential of machine learning and data-driven approaches. Future research directions are outlined, emphasizing the need for more scalable algorithms, real-time scheduling capabilities, improved user interfaces, and comprehensive validation studies. This review contributes to the advancement of PSP optimization, aiming to enhance healthcare workforce management, improve patient care, and ultimately address the pressing challenges faced by healthcare systems worldwide, thus supporting the achievement of SDG 3 and promoting universal health coverage.https://ieeexplore.ieee.org/document/10819388/Physician schedulingpersonnel schedulingsystematic literature reviewcombinatorial optimizationoperational researchsustainable development goals |
| spellingShingle | Norizal Abdullah Masri Ayob Meng Chun Lam Nasser R. Sabar Graham Kendall Mohamad Khairulamirin Md Razali Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future Directions IEEE Access Physician scheduling personnel scheduling systematic literature review combinatorial optimization operational research sustainable development goals |
| title | Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future Directions |
| title_full | Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future Directions |
| title_fullStr | Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future Directions |
| title_full_unstemmed | Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future Directions |
| title_short | Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future Directions |
| title_sort | optimization techniques for physician scheduling problem a systematic review of recent advancements and future directions |
| topic | Physician scheduling personnel scheduling systematic literature review combinatorial optimization operational research sustainable development goals |
| url | https://ieeexplore.ieee.org/document/10819388/ |
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