Indicators that provide information about (a) the increasing spatial and decreasing temporal separation of production, consumption and waste management, (b) constraints in raw materials supply and (c) marginal changes in money and time spent should be available to product designers and consumers.
The optimization objectives include economic impacts characterized by recycling income and cost components for waste management, while the constraint set consists of mass balance, capacity limitation, recycling limitation, scale economy, conditionality, and relevant screening constraints.
He et al. ([2008a]) developed an interval full-infinite programming (IFIP) method through introduction of functional intervals into an optimization framework and applied it to waste management planning with infinite objectives and constraints under uncertainty.
Sun et al. ([2012]) developed an inexact piecewise-linearization-based fuzzy flexible programming (IPFP) model to tackle nonlinear economies-of-scale effects in interval-parameter constraints for a representative waste management problem.
Li et al. ([2009c]) developed inexact fuzzy-stochastic constraint-softened programming for waste management through incorporation of multistage stochastic programming (MSP), ILP, and FFP.
This paper presents results from a survey of waste management authorities aimed at identifying the opportunities and constraints associated with implementing economic instruments (EIs) for solid waste management (SWM) in South Africa (SA).
Specifically, pollution and impacts, induced by the overall solid waste management system, are considered through the formalization of constraints on incineration emissions and on negative effects produced by disposal or other particular treatments.
The IPRO model can help decision makers to identify desired waste management policies under various environmental, economic, system-feasibility and system-reliability constraints.
Xu et al. [14] developed a robust hybrid stochastic chance-constraint programming model for supporting municipal solid waste management under uncertainty.
More recently, Xu et al. ([2009]) proposed hybrid stochastic robust chance-constraint programming (SRCCP) for supporting municipal solid waste management, which couples stochastic robust programming with CCP.
