Grey Chance-Constrained Programming: Application to Regional Solid Waste Management Planning

This paper introduces a grey chance-constrained programming (GCCP) method by incorporating the advantages of grey mathematical programming and stochastic mathematical programming methods within a general optimization framework. The method is an improvement upon previous chance-constrained programming and grey linear programming methods in terms of both its technical characteristics and its applicability. Distribution information in B and uncertainties in A and C can all be effectively captured in the optimization process, and the proposed GCCP solution algorithm is applicable to practical problems since it does not lead to more complicated intermediate models. The method is applied to a hypothetical planning problem of waste flow allocation within a regional solid waste (RSW) management system. The results obtained indicate that reasonable and useful grey solutions and thus decision alternatives can be generated under different probabilities of violating the system constraints.