No abstract

Motivated by some real stochastic service systems, we study a feedback retrial queue with customer balking and unreliable servers (servers subject to breakdowns). A customer will either join a retrial orbit or balk if all servers are busy upon his arrival. After a service completion, the customer either leave the system or enter the retrial group again for additional service. The customer who is interrupted by the server failure will be served by another server if available; otherwise, he will be contacted later and treated as leaving the system. We use a quasi-birth-and-death process to analyze this system with almost all possible uncertain factors and derive major performance measures of the system. We also develop a cost function determining the optimal parameter settings of the system under the stability condition. Probabilistic Global Search Lausanne approach is employed in solving the optimization issue. The effects of parameters on performance measures of the system are examined numerically. Finally, we discuss the application of the model in the telephone medical consulting service system.

Presently, the total supply of crude oil is sufficient, but short-term supply and demand imbalances and regional imbalances still exist. The effect of crude oil supply security and price impact cannot be ignored. As the world's largest oil importer, China is highly dependent on foreign oil. Therefore, the fluctuation of international oil prices may impact the development of China's various industries in a significant and differential way. However, because the available data have different frequencies, much of the recent research that addresses the effect of oil prices on industry development need to replace, split, or merge the original data, resulting in loss of the information from the original data. Using the mixed data sampling model (MIDAS(m,K,h)-AR(1)) with the first-order lag autoregressive terms of the interpreted variables, this study builds a mixed data model to investigate the effect of oil price volatility on the output of China's industries. This study expands the extant research by financial market fluctuations and macroeconomic analysis, and at the same time makes short-term predictions on the output of China's seven main industries. The analysis results show that the mixed data regression model brings the original information contained in different frequency data into the model analysis, and utilizes the latest high frequency data of the explanatory variables to perform real-time short-term prediction of low-frequency interpreted variables. This method improves the timeliness of forecasting macroeconomic indicators and the accuracy of short-term forecasts. The empirical results show that the spot price of international crude oil has a significant and differential impact on the outputs of the seven industries in China. Among them, oil price fluctuation has the greatest impact on the output of China's financial industry.