Efficient simulation budget allocation for selecting the best set of simplest good enough designs

Simple designs have many advantages compared with complex designs, such as requiring less computing and memory resources, and easier to interpret and to implement. Therefore, they are usually more preferable than complex designs in the real world if their performances are within a good enough range. In this paper, we propose an algorithm OCBA-bSG to identify a best subset of m simplest and good enough designs among K (K >; m) total designs. The numerical results show that our approach allocates the simulation budget efficiently, and outperforms some other approaches on the test problems.