HIGHLIGHTS
SUMMARY
Using these approximations, evaluation of the expected profit is fast and classical optimization methods (e_g, dynamic programming, genetic algorithms) can be applied to optimize the UPHES scheduling within a reasonable time budget. This motivates the authors to investigate the use of surrogate models to (partially) replace the time-consuming simulator, in conjunction with parallel evaluations of the expected profit. And despite the potentially larger number of simulations gained by parallelization, the final outcome of the algorithms is often worse with high batch sizes (8 or 16 compared to 2 or 4) within a fixed . . .
If you want to have access to all the content you need to log in!
Thanks :)
If you don't have an account, you can create one here.