Multi-fidelity surrogate-based optimization for decomposed buffer allocation problems

The buffer allocation problem (BAP) for flow lines has been extensively addressed in the literature. In the framework of iterative approaches, algorithms alternate an evaluative method and a generative method. Since an accurate estimation of system performance typically requires high computational effort, an efficient generative method reducing the number of iterations is desirable, for searching for the optimal buffer configuration in a reasonable time. In this work, an iterative optimization algorithm is proposed in which a highly accurate simulation is used as the evaluative method and a surrogate-based optimization is used as the generative method. The surrogate model of the system performance is built to select promising solutions so that an expensive simulation budget is avoided. The performance of the surrogate model is improved with the help of fast but rough estimators obtained with approximated analytical methods. The algorithm is embedded in a problem decomposition framework: several problem portions are solved hierarchically to reduce the solution space and to ease the search of the optimum solution. Further, the paper investigates a jumping strategy for practical application of the approach so that the algorithm response time is reduced. Numerical results are based on balanced and unbalanced flow lines composed of single-machine stations.