Municipal Solid Waste Plannining And Management Under Uncertainty

Municipal solid waste (MSW) generation and management issues have increasing bearing on the socio-economic, human health, aesthetics and amenity of many communities, states and nations around the world, which poses a challenge with regard to environmental quality and sustainable development. It is thus deemed necessary to develop a systems approach for analyzing waste management, so as to support decisions of waste-management operation and planning. However, complexities exist in such a MSW management system, including the collection techniques to be used, the levels of service to be offered, and the facilities to be adopted; Furthermore, uncertainties may exist in the related cost parameters, capacity limitations, waste-generation rates, and waste diversion goals. Therefore, the objective of this study is to apply inexact optimization methods to MSW management for handling with such complexities and uncertainties. In this paper, an interval-parameter chance-constrained dynamic programming (ICDP) method is first developed for the capacity planning of an integrated MSW management system in the city of Regina under uncertainty. Then, a two-stage support-vector-regression optimization model (TSOM) is developed for planning of MSW management in the urban districts of Beijing, China; the model can solve the problem that the input data of optimization model is not predicted with a high accuracy. Finally, a simulation based two-stage interval stochastic programming (STIP) model is developed for planning of MSW management in the urban districts of Beijing, China; the model can solve the problem providing the two-stage stochastic programming with probability distribution functions of the input data. The results indicate that ICDP method can effectively reflect the system's uncertainties, and it can assist decision makers to exam the risk of violations of facility-capacity constraints, and reveal the tradeoff between system-cost and constraint-violation risk; TSOM can effectively reflect the system's uncertainties, and it can predict the waste generation rate as the inputd of integer linear programming for more objective solution; STIP model simulates the probability distribution functions of waste generation rate which provide the random parameters of two-stage stochastic programming model.