Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future Directions

Abstract

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.