An inexact two-stage mixed integer linear programming model for waste management under uncertainty

Abstract

This paper introduces a hybrid optimization approach, an inexact two-stage mixed integer linear programming (ITMILP) model, for the planning of regional solid waste management systems under uncertainty. The model improves upon the existing mixed integer, two-stage stochastic and interval-parameter programming approaches by allowing uncertainties presented as random distributions and discrete intervals, as well as policies expressed as allowable waste-loading targets to be effectively incorporated within a general optimization framework. In the modeling formulation, penalties are imposed when the policies are violated. In its solutions algorithm, the ITMILP model is transformed into two deterministic submodels, which were solved sequentially. Application of the developed methodology to the planning of a waste management system indicates that reasonable solutions for the binary and continuous decision variables can be generated through this approach. Considerable information was generated regarding decisions of facility expansion within a multi-period, multi-scale and multi-waste-level context; and optimal waste flow allocation patterns were achieved within the waste management system. The ITMILP model was then employed to generate a number of decision alternatives under various policy conditions, allowing for more in-depth analyses of tradeoffs between environmental and economic objectives.

Keywords:

• Decision-making
• Integer programming
• Management
• Solid waste
• Stochastic programming
• Uncertainty

Acknowledgement

This study was supported by the National High Technology Research Foundation (No. 2001AA644020), the Natural Science Foundation (No. 50225926 and 70171055), the Outstanding Scholars Program of CAS, and the Natural Science and Engineering Research Council of Canada.