Initial sampling using multi-fidelity information in simulation optimization of manufacturing systems

High-fidelity models are capable of providing accurate estimates but slow in execution. On the other hand, estimates provided by low-fidelity models are biased but fast. The knowledge embedded in low-fidelity models might be helpful for simulation optimization algorithms. Several multi-fidelity modeling algorithms have been proposed in literature, whereas currently only high-fidelity information is used in the initial sampling phase. This poster provides an algorithm to allocate high-fidelity budgets using multi-fidelity information in order to contain a fixed number of good solutions in the initial design. Results show that the proposed sampling policy can allocate more budgets in promising areas.