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In this study, a two-stage interval-stochastic mixed integer programming method was developed for supporting long-term planning of solid waste management in the city of Beijing, China. The developed method reflects uncertainties expressed as probability density functions and intervals, as well as offers a linkage between predefined environmental policies and associated economic implication. The method has advantages in tackling dynamic, interactive, and uncertain characteristics of solid waste management system in the city, and addressing issues regarding waste diversion and landfill prolongation. Reasonable solutions were generated for waste flow allocation and system capacity expansion. Data envelopment analysis was then utilized for analyzing these solutions under different policy scenarios. Obtained results can provide useful information and decision-support for the city's solid waste management planning. Results are valuable for adjustment of the existing waste management practice and identification of desired waste flow allocation patterns for the city of Beijing. Results also suggest that the developed method is applicable to other engineering decision-making problems. [ABSTRACT FROM AUTHOR]

Abstract: In this study, a GFIPMIP (grey-forecasting interval-parameter mixed-integer programming) approach was developed for supporting IEEM (integrated electric-environmental management) in Beijing. It was an attempt to incorporate an energy-forecasting model within a general modeling framework at the municipal level. The developed GFIPMIP model can not only forecast electric demands, but also reflect dynamic, interactive, and uncertain characteristics of the IEEM system in Beijing. Moreover, it can address issues regarding power supply, and emission reduction of atmospheric pollutants and GHG (greenhouse gas). Optimal solutions were obtained related to power generation patterns and facility capacity expansion schemes under a series of system constraints. Two scenarios were analyzed based on multiple environmental policies. The results were useful for helping decision makers identify desired management strategies to guarantee the city's power supply and mitigate emissions of GHG and atmospheric pollutants. The results also suggested that the developed GFIPMIP model be applicable to similar engineering problems. [Copyright &y& Elsevier]