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In healthcare, the planning and the management of resources are challenging as there are always many complex and stochastic factors in both demand and supply. Simulation Optimization (SO) that combines simulation analysis and optimization techniques is well suited for solving complicated, stochastic, and mathematically intractable decision problems. In order to comprehensively unveil the degree to which SO has been used to solve healthcare resource planning problems, this article reviews the academic articles published until 2021 and categorizes them into multiple classification fields that are related to either problem perspectives (i.e., healthcare services, planning decisions, and objectives) or methodology perspectives (i.e., SO approaches and applications). We also examine the relations between the individual fields. We find that emergency care services are the most applied domain of SO, and that discrete-event simulation and random search methods (especially genetic algorithms) are the most frequently used methods. The literature classification can help researchers quickly learn this research area and identify the publications of interest. Finally, we identify major trends, insights and conclusions that deserve special attention when studying this area. We suggest many avenues for further research that provide opportunities for expanding existing methodologies and for narrowing the gap between theory and practice.

Simulation optimization in healthcare resource planning: A literature review