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Under uncertainty of exchange rate, we extend the batch process production model of Lin et al. (2002) by considering an export-oriented manufacturer making decisions to switch freely between domestic and foreign locations. The export-oriented manufacturer is risk neutral and has rational expectations. We use dynamic programming and Lagrange multiplies for a stochastic optimization control problem to get the productive value of exporter produces in domestic and foreign locations. Next, the export-oriented manufacturer can make decision regarding the optimal entry (exit) trigger for transferable locations wherever the product locations are. It provides the supplier with another way to make decisions.

This study considers the effects of two real exchange rates on strategies that govern the locations of production by firms that are entering two foreign countries. The batch process production model of Lin and Wu (Asia-Pacific Journal of Operational Research, 21, 2004, 35–52), which considers two locations of production, one in each of two countries is extended to develop a decision valuation model to choose the two optimal locations to produce a good — one in each country. This extended model applies the Real Options Analysis (ROA) to determine the value of locating production in three countries. Moreover, a closed-form solution to the Continuous-Time Model Optimization Problem is derived. The optimal entry trigger and expected arrival time of an exporter from country-0 to country-1 or 2 are calculated; a sensitivity analysis is performed, and some characteristic strategies of the operating method for the Cobb–Douglas batch process model among three countries are determined. A useful summary of insights is provided for global managers.

This study considers the effects of one real exchange rate on strategies that govern locations of production by firms that are entering N - 1 foreign countries. The batch process production model (Lin, CT and CR Wu (2004a). Asia Pacific Journal of Operational Research, 21, 35–52) which considers two locations of production, one in each of two countries is extended to develop a decision valuation model to choose the two optimal locations to produce a good — one in each country. This extended model applies the real options approach (ROA) to determine the value of locating production in N countries. Moreover, a closed-form solution to the Continuous-Time Model Optimization Problem is derived. The optimal entry threshold value of a firm from country-0 to country-(N - 1) is calculated; a sensitivity analysis is performed, and some characteristic strategies of the operating method for the Constant Elasticity of Substitution (CES) batch process model among N countries are determined. Next, we can get optimal entry threshold value for Cobb-Douglas, perfect substitution and Leontief by CES production function. A useful summary of insights is provided for global managers.

To ensure safety of a batch process and quality of its final product, one needs to quickly identify an assignable cause of a fault. To solve the diagnosis problem of a batch process, Cho and Kim⁶ proposed a new statistical diagnosis method based on Fisher discriminant analysis (FDA). They showed satisfactory diagnosis performance on industrial batch processes. However, the diagnosis method of Cho and Kim⁶ has a major limitation: it does not work when the fault data available for building the discriminant model are insufficient. In this work, we propose a method to handle the insufficiency of the fault data in diagnosing batch processes. The diagnosis performance of the proposed method is demonstrated using a data set from a PVC batch process. The proposed method is shown to be able to handle the data insufficiency problem, and yield reliable diagnosis performance.

A novel multivariable statistical process control (MSPC) method based on the dissimilarity analysis is proposed for on-line batch process monitoring. The dissimilarity of data between the current batch run and a preassigned reference batch run is used as monitoring index. The possibility density function of the dissimilarity index distribution is estimated by kernel density function and the control limits are computed. The on-line monitoring is realized through utilizing the Kalman filter method to estimate the whole trajectory of the current batch run. The monitoring performance of the proposed method is compared with the traditional multiway principal component analysis (MPCA) method on a fed-batch penicillin fermentation process. The results have shown that the proposed method performs well to successfully detect the faults in batch processes.