Multi-fidelity surrogate-based optimization for decomposed buffer allocation problems
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Multi‑fidelity surrogate‑based optimization for decomposed buffer allocation problems Ziwei Lin1,2 · Nicla Frigerio2 · Andrea Matta2 · Shichang Du1 Received: 20 December 2019 / Accepted: 12 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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. Keywords Buffer allocation problem · Multi-fidelity surrogate modeling · Simulation optimization
* Nicla Frigerio [email protected] 1
Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China
2
Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
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1 Introduction A production system can be seen as a set of resources interconnected by a material handling system where work-in-process might be held in buffers between two sequential stations. These buffers of parts help in reducing the propagation of blocking and starvation phenomena along the production system. However, dedicating space to maintain interoperative inventories is costly and extends the production lead time. For these reasons, the buffer allocation problem (BAP) is an optimization problem of high importance for industries where there is a trade-off between productivity criteria and design and management costs. The classical primal BAP considers the total allocated buffer capacity as the objective function and the throughput satisfaction as a constraint, and this is known in the literature as the primal problem (Gershwin and Schor 2000). The dual problem, also common in the literature, maximizes the throughput under a constrained buffer capacity. This paper focuses on the primal problem. Furthermore, we address proble
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