Capacity Planning for an Integrated Waste Management System Under Uncertainty: a North American Case Study (original) (raw)

In this paper, a grey integer-programming (GIP) formulation for the capacity planning of an integrated waste management system under uncertainty is applied to a North American case study. The GIP model is formulated by introducing concepts of grey systems and grey decisions into a mixed integer linear programming (MILP) framework. The approach has an advantage in that uncertain information (presented as interval numbers) can be effectively communicated into the optimization processes and resulting solutions, such that feasible decision alternatives can be generated through interpretation and analysis of the grey solutions according to projected applicable system conditions. Moreover, the GIP solution algorithm does not lead to more complicated intermediate models, and thus has lower computational requirements than other integer-programming methods that deal with uncertainties.