Genetic algorithm based on weighted goal programming for doctor rostering problem

Purpose: In the healthcare sector, undisrupted service is essential for hospitals. Therefore, shift work plays a vital role in satisfying constraints such as coverage requirements and government regulations. The doctor rostering problem is classified as an NP -hard problem due to its complexity and scale. In addition to the fairness of assignments, including hospital management policies, and government regulations, many related factors must be taken into account during the scheduling process in this scheduling problem. Theory and Methods: This study aims to generate a rostering system that can satisfy the requirements of the hospital, ensure fairness amongst the doctors, and take preferences into account. A genetic algorithm based on a weighted goal programming model was proposed to solve the doctor rostering problem. The proposed model was applied to the internal diseases department and the lateral branches department of a training and research hospital. Results: 15 different scenarios were constructed, considering different problem scales and different preference patterns of the doctors that may occur in the future. It is approved that the proposed algorithm can be applied to different problem scales and conditions. The parameters of the proposed algorithm were calibrated with an experimental design method. Conclusion: In this study, two main contributions were presented. A model with new constraints was introduced for researchers. In addition, a genetic algorithm based on weighted goal programming was proposed to solve the problem and applied to a real -world case study.