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As a semiconductor supply chain becomes widespread and the competition pressure is very fierce, the detrimental effects of increasing varieties and variations are magnified in the supply chain. But many important issues, such as different service priorities, adaptability, controllability and scalability of performance metrics, have not been addressed in the literature. Conventional modelling techniques of supply chain operations are no longer effective for supply chain configuration. Therefore, a proposed empirical model was first built to catch up the relationship between supply-chain configuration and metrics under the influence of the variability sources. Next, an optimal supply chain configuration model is formulated as a polynomial goal programming model to accommodate different goal objectives. Finally, an efficient solution methodology is further developed to find out the optimal supply chain configuration. Our results show that our proposed approach can easily be adapted to the practices in semiconductor supply chain, and the solution methodology developed in this paper is truly promising.

Nonlinear dynamical systems often generate significant amounts of observational data such as time series, as well as high-dimensional spatial data. To delineate recurrence dynamics in the spatial data, prior efforts either extended the recurrence plot, which is a widely used tool for time series, to a four-dimensional hyperspace or utilized the network approach for recurrence analysis. However, very little has been done to differentiate heterogeneous types of recurrences in the spatial data (e.g., recurrence variations of state transitions in the spatial domain). Therefore, we propose a novel heterogeneous recurrence approach for spatial data analysis. First, spatial data are traversed with the Hilbert Space-Filling Curve to transform the variations of recurrence patterns from the spatial domain to the state-space domain. Second, we design an Iterated Function System to derive the fractal representation for the state-space trajectory of spatial data. Such a fractal representation effectively captures self-similar behaviors of recurrence variations and multi-state transitions in the spatial data. Third, we develop the Heterogeneous Recurrence Quantification Analysis of spatial data. Experimental results in both simulation and real-world case studies show that the proposed approach yields superior performance in the extraction of salient features to characterize and quantify heterogeneous recurrence dynamics in spatial data.

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