Industry-environment system management based on an uncertain Gaussian diffusion optimization model for coal-dependent cities in ecologically fragile areas
2019
In this study, an industry-environment system management based on uncertain Gaussian diffusion optimization model (IEM-UGOM) was formulated to handle administrant issues of coal-dependent cities in ecologically fragile areas. The proposed model based on interval two-stage credibility-constrained full-infinite programming (ITFP) method and Gaussian diffusion model. The method was integrated by interval two-stage stochastic programming (ITSP), fuzzy credibility constrained programming (FCCP) and interval full-infinite programming (IFIP), which can tackle multiple uncertainties in terms of probability distribution, possibility distribution, crisp intervals and functional intervals. In addition, Gaussian diffusion model can be used to predict pollutant concentrations under different wind velocities. Then, IEM-UGOM was applied for the administration of industry-environment system (IES) in Yulin City, a typical ecologically vulnerable coal-dependent city in China, by considering 7 key industries, 3 pollutants, 5 periods, 7 credibility satisfaction levels and 4 wind velocity levels. Meanwhile, retreatment efficiency and transfer factors are considered as well. Results of system benefits, production reduction, pollutants excess emission amounts and penalties, pollutants retreatment amounts and costs under various credibility satisfaction levels and wind velocities are generated. Overall, IEM-UGOM is proved to be an efficient way on this study for reflecting the decision maker's attitude toward various adjustment scenarios of industrial production and environmental protection, and could help seek cost-effective management strategies under various credibility satisfaction levels and wind velocities.
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