Conclusion: SA and GA can be used to solve a large and complex SDDP as demonstrated in the primary hypertension case study. They can find the optimal or near optimal solutions efficiently where the key parameters are properly set. The optimal parameter setting is problem specific and requires a tuning procedure considering various scenarios with different sets of parameters. RL needs further investigation to improve the performance possibly by using more complicated RL methods or in a different structure of the underlying evaluation model. This study can be extended to construct the underlying evaluation model using a DES and to technically improve the optimisation methods. Producing the data relevant to SDDPs will also help to make better informed decisions for SDDPs in health technology appraisal.
