Inexact Optimization Methods And Its Application To Municipal Solid Waste Management

Recently, with the rapid development of socio-economic and improvement of human living standard, the increasing waste generation and environmental stress lead to the shortage of the land resources near urban centers and serious environmental pollution problems. Therefore, the effective municipal solid waste management should be considered various factor overall, such as society, economy, environment, and resource. In MSW management systems, there are many processes that should be considered by decision-makers, such as waste collection, transportation, treatment, and disposal. Moreover, many system parameters (e.g., waste-generation rate, waste treatment cost, and facility capacity), impact factors (e.g., energy price, labor fee, and management expenses) and their interactions are associated with uncertainties. Furthermore, the spatial and temporal variations of many system components may further multiply these uncertainties. These complex uncertainties could result in difficulties in the long-term planning of MSW activities. Therefore, the objective of this study is to develop an inexact waste management model for dealing with such complexities and uncertainties and supporting municipal solid waste management in the City of Changchun, China. In this study, the ITWM model will be based on the techniques of interval-parameter programming (IPP) and two-stage stochastic programming (TSP); the IBRA model will be based on the techniques of interval-parameter programming (IPP) and minimax regret (MMR) analysis. The results indicate that ITWM and IBRA could effectively reflect complexities and uncertainties, and provide decision makers with scientific basis for decision making.