Grey fuzzy integer programming: An application to regional waste management planning under uncertainty

This paper introduces a grey fuzzy integer programming (GFIP) method and its application to regional solid waste management planning under uncertainty. The GFIP improves upon the existing integer programming methods by incorporating both grey fuzzy linear programming (GFLP) and grey integer programming (GIP) approaches within a general optimization framework. The approach allows uncertainty in both model coefficients and stipulations to be effectively communicated into the optimization process and resulting solutions, such that feasible decision alternatives can be generated through appropriate interpretation of the solutions. Moreover, the GFIP does not lead to more complicated intermediate models in its solution process, thus offering lower computational requirements than existing methods. In addition, it is applicable to practical problems.The modelling approach is applied to a hypothetical planning problem of waste management facility expansion/utilization planning within a regional solid waste (RSW) management system. The results indicate that reasonable solutions were generated for both binary and continuous variables. The binary variable solutions represent the related grey decisions of waste management facility expansion within a multi-period, multi-facility and multi-scale context. Further, they have been interpreted to provide decision alternatives that reflect the effects of uncertainties. The continuous variable solutions relate to grey decisions for waste flow allocation corresponding to the suggested facility expansions.