CAPACITY PLANNING FOR AN INTEGRATED WASTE MANAGEMENT SYSTEM UNDER UNCERTAINTY: A NORTH AMERICAN CASE STUDY

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. The proposed model is used for the long-term planning of waste management facility expansion/utilization in the Regional Municipality of Hamilton–Wentworth (RMHW), Ontario, Canada. The binary decision variables in the model represent the ranges of facility expansion/development alternatives within a multi-period, multi-facility and multi-scale context, and the grey continuous variables represent waste flows along the routes connecting the municipalities and the waste management facilities. The results indicate that reasonable solutions have been generated through this grey mathematical programming approach. The case study results are potentially useful for MSW decision-makers in the RMHW for the long-term planning of the Region's waste management activities and for formulating related local policies/regulations regarding waste generation and management, and may stimulate the interest of waste management professionals in other jurisdictions on the use of this type of modelling approach for their specific long-range planning applications.