We consider a serial production system with machine breakdowns, where the machine at each station alternates between up and down states with exponential up and down time distributions. To obtain insight about the opti...We consider a serial production system with machine breakdowns, where the machine at each station alternates between up and down states with exponential up and down time distributions. To obtain insight about the optimal production strategy, we focus on one production station. Its production process (output) and that of the previous station (input) are modeled by point processes with stochastic intensities. Our objectives is to control both input and output intensities such that expected discounted profit is maximized. We show that the optimal control policy is of a threshold type which is characterized by two threshold values.When each station in the serial system has reliable machines, the well known Kanban production strategy, which was first developed in Toyota Motor Co. of Japan, is usually used to control the production process at each station. Our result in this paper implies that, among other things, the traditional Kanban control rule has to be modified if the production environment is random.展开更多
Due to its complicated matrix effects, rapid quantitative analysis of chromium in agricultural soils is difficult without the concentration gradient samples by laser-induced breakdown spectroscopy. To improve the anal...Due to its complicated matrix effects, rapid quantitative analysis of chromium in agricultural soils is difficult without the concentration gradient samples by laser-induced breakdown spectroscopy. To improve the analysis speed and accuracy, two calibration models are built with the support vector machine method： one considering the whole spectra and the other based on the segmental spectra input. Considering the results of the multiple linear regression analysis, three segmental spectra are chosen as the input variables of the support vector regression （SVR） model. Compared with the results of the SVR model with the whole spectra input, the relative standard error of prediction is reduced from 3.18% to 2.61% and the running time is saved due to the decrease in the number of input variables, showing the robustness in rapid soil analysis without the concentration gradient samples.展开更多
文摘We consider a serial production system with machine breakdowns, where the machine at each station alternates between up and down states with exponential up and down time distributions. To obtain insight about the optimal production strategy, we focus on one production station. Its production process (output) and that of the previous station (input) are modeled by point processes with stochastic intensities. Our objectives is to control both input and output intensities such that expected discounted profit is maximized. We show that the optimal control policy is of a threshold type which is characterized by two threshold values.When each station in the serial system has reliable machines, the well known Kanban production strategy, which was first developed in Toyota Motor Co. of Japan, is usually used to control the production process at each station. Our result in this paper implies that, among other things, the traditional Kanban control rule has to be modified if the production environment is random.
基金Supported by the National High-Technology Research and Development Program of China under Grant Nos 2014AA06A513 and 2013AA065502the National Natural Science Foundation of China under Grant No 61378041the Anhui Province Outstanding Youth Science Fund of China under Grant No 1508085JGD02
文摘Due to its complicated matrix effects, rapid quantitative analysis of chromium in agricultural soils is difficult without the concentration gradient samples by laser-induced breakdown spectroscopy. To improve the analysis speed and accuracy, two calibration models are built with the support vector machine method： one considering the whole spectra and the other based on the segmental spectra input. Considering the results of the multiple linear regression analysis, three segmental spectra are chosen as the input variables of the support vector regression （SVR） model. Compared with the results of the SVR model with the whole spectra input, the relative standard error of prediction is reduced from 3.18% to 2.61% and the running time is saved due to the decrease in the number of input variables, showing the robustness in rapid soil analysis without the concentration gradient samples.
