An inexact stochastic quadratic programming method for municipal solid waste management

The existences of nonlinearities and uncertainties are the main complexities that cause difficulties in planning municipal solid waste-management systems. In this study, an inexact stochastic quadratic programming method is developed for handling nonlinearities in the cost objective to reflect the economies of scale and uncertainties expressed as probability distributions and discrete intervals. This model improves upon the conventional inexact quadratic programming and two-stage stochastic programming approaches. It can better reflect system cost variations and generate more reasonable and applicable solutions. It can also be used for analysing various policy scenarios that are associated with different levels of penalties when the promised policy targets are violated. The developed method is applied to a case of long-term waste-management planning. The interactive and derivative algorithms are employed for solving the developed model. The solutions are presented as combinations of deterministic, interval and distributional information. They can be used for generating decision alternatives and thus help waste managers to identify desired policies under various environmental, economic and system-reliability constraints.