Abstract
In this research, Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling (MO2TOS) is exploite to solve the simultaneous scheduling problem of machines and automated guided vehicles (AGVs) in flexible manufacturing system (FMS). Fidelity represented the degree to which a simulation replicates reality. Because of FMS contains lots of system characteristics and flexibilities, and there are many different fidelity models exist which considerd different system features. Evaluating system via higher fidelity model can be more accurately, but it will cause time-consuming and will bring higher cost. Although lower fidelity model may suffered bias, but faster evaluation and the performance can provide partial of trend between low and high fidelity models. It is important to enhance the efficiency of optimization by using multi-fidelity models. In MO2TOS, applied an inappropriate sampling method will lead a poor quality of optimization. Hence Adaptive Sampling is proposed to update the sampling method by sample correlation coefficient. The correlation coefficient can present the trend between multi-fidelity models, updating the sampling method according to the sample correlation coefficient can enhance the efficiency of optimization and save 45.39% and 22.71% of simulation resources for higher and lower correlation models. Grouping method is one of main factors which may affect the quality of optimization significantly. In this research, Adaptive Grouping is proposed to update the group after every iteration of MO2TOS. It may significantly enhance the gap between groups, further to allocate resource effectively and save 28.45% and 17.94 of simulation resources for higher and lower correlation models.