Search

With a service for the most developed economy and dense population in China, the water use of Guangdong province shows distinct regional difference and is subject to multiple driving forces. The regional differences of total water use (TWU) and water use efficiency (WUE) for Guangdong province and its four sub-regions (i.e. Pearl River Dealt region (PRD), Eastern Wing (YD), Western Wing (YX), and Northern Mountain Region (YB)) were quantified by Theil index, and the influence of various variables on WUE was evaluated through multiple linear regression (MLR) models. Overall, Theil index of TWU showed a decreasing trend whereas Theil index of WUE increased in recent decades, suggesting that Guangdong province has experienced an enlarging regional difference of WUE along with a gradually weakened regional difference of TWU. The PRD has the most significant regional differences of WUE and TWU and accounts for a predominated proportion in the total regional difference. Theil indexes of GDP of industry, per capita GDP and per capita value-added by agriculture had positive regression coefficients and were found to have the most significant impact on the regional difference of WUE. This study has the potential to promote a balanced and coordinated regional development in terms of even regional WUE and TWU. [ABSTRACT FROM AUTHOR]

Simulation modeling is one of the most useful techniques to analyze and evaluate the dynamic behavior of the complex manufacturing systems. Combining the mathematical power of an analytical method and the modeling capability of simulation with optimization approach called hybrid simulation-analytical modeling has been presented rarely. In this study a production control model is developed for a paint shop department in an automotive company in Turkey. As a real case study, the optimum operating setting of a paint shop production line of automotive company is determined using hybrid simulation optimization approach. In the optimization stage of the study Design of Experiment (DoE) is used to identify critical variables of the system by fitting a polynomial to the experimental data in a multiple linear regression analysis. The meta-model is validated and shown that it provides good approximations to simulation results. Findings from hybrid simulation-analytical optimization approach give invaluable knowledge to the company for the re-designing and control of current manufacturing system to increase its productivity. [ABSTRACT FROM AUTHOR]