We model and apply a stochastic-simulation-based methodology to optimize the machine allocation of a flexible flow shop (FFS) dedicated to integrated circuit (IC) packaging. This contrasts with most previous research on non-deterministic FFS problems wherein stochastic simulation is mostly used to estimate throughput, cycle time, delay cost, or some other measure(s) in order to compare the performances of already-existing heuristic-based algorithms. The methodology applied in this research, called progressive simulation metamodeling for IC Packaging (IC-PSO), while rooted in the traditional metamodeling technique known as Response Surface Methodology (RSM), contrasts with RSM in that it is equipped with well-designed mechanisms to ensure an ever-increasing solution quality in an attempt to achieve the desirable optimality. The computational efficiency that IC-PSO affords IC packaging companies is demonstrated via a numerical study. Meanwhile, an empirical study based on real data was conducted to validate the viability of the proposed methodology in real settings.
While nearly all previous algorithms designed to solve simulation optimization problems have treated the outputs of simulation systems at a given design point (input parameter) as being independent of each other, this premise is flawed in that simulated outputs are generally correlated. We propose a decorrelation (DC) procedure that can effectively evaluate and remove the correlation of outputs of a simulation system. The proposed DC procedure is further integrated with STRONG, an improved framework of the well-known Response Surface Methodology (RSM), for tackling the simulation optimization problems with correlated outputs. This integration is particularly synergistic due to the fact that STRONG is a fully automated, response-surface-based procedure possessing appealing convergence properties and DC can take advantage of the concept of trust region as in STRONG to enable the removal of the correlation of outputs at the design points within the same trust region all at once. This is more efficient compared to the traditional approaches where a substantial number of observations are typically required for dealing with correlations. The resulting integrated method, which we call STRONG-DC, requires various adaptations so as to ensure the efficacy and efficiency of the overall framework. STRONG-DC preserves the desirable automation and convergence as STRONG, namely, it does not require human involvements and can be proved to achieve the truly optimal solution(s) with probability one (w.p.1) under reasonable conditions. Moreover, the effectiveness and efficiency of STRONG-DC are evaluated through extensive numerical analyses, along with a case study involving the well-known newsvendor problem.
Automated material handling systems (AMHS) have been widely used in semiconductor manufacturing. However, the performance of AMHS heavily hinges on vehicle fleet sizing, which is a complex yet crucial problem. For example, a small fleet size may increase the average wait time, but a large fleet size can also result in traffic congestion. This tradeoff is difficult and can be further exacerbated by profound uncertainty in the manufacturing process. In the literature, the existing models are focused on improving the mean-based performance of AMHS, where the resulting optimal vehicle fleet size is fixed, lacking the ability and flexibility to respond to the changes and/or special requirements that suddenly come up in the manufacturing process. Another drawback with the existing models is that they are not able to characterize the upside/downside risks associated with the resulting vehicle fleet size. This paper, motivated by a real project, presents a novel quantile-based decision model to fill the gap. The adjustment of [Formula: see text] values in the proposed decision model allows for agile vehicle fleet sizing according to the production situations, resulting in the satisfactory performance of AMHS. We develop a simulation optimization solution method, called ES-AMHS in short, to enable the efficient derivation of the optimal vehicle fleet size. A comprehensive numerical analysis is conducted to evaluate the efficiency and efficacy of the solution method. Finally, an empirical study in cooperation with a wafer fab in Taiwan is presented to show the practical usefulness of this methodology in a real-world setting.
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