GREY FUZZY INTEGER PROGRAMMING - AN APPLICATION TO REGIONAL WASTE MANAGEMENT PLANNING UNDER UNCERTAINTY

This paper introduces a grey fuzzy integer programming (GFIP) method a nd its application to regional solid waste management planning under u ncertainty. The GFIP improves upon the existing integer programming me thods by incorporating both grey fuzzy linear programming (GFLP) and g rey 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 alternat ives can be generated through appropriate interpretation of the soluti ons. Moreover, the GFIP does not lead to more complicated intermediate models in its solution process, thus offering lower computational req uirements than existing methods. In addition, it is applicable to prac tical problems. The modelling approach is applied to a hypothetical pl anning problem of waste management facility expansion/utilization plan ning within a regional solid waste (RSW) management system. The result s indicate that reasonable solutions were generated for both binary an d continuous variables. The binary variable solutions represent the re lated grey decisions of waste management facility expansion within a m ulti-period, multi-facility and multi-scale context. Further, they hav e been interpreted to provide decision alternatives that reflect the e ffects of uncertainties. The continuous variable solutions relate to g rey decisions for waste flow allocation corresponding to the suggested facility expansions.