Optimization model for solid waste allocation planning

Abstract

An interval chance-constrained mixed integer linear programming (ICCMILP) was developed for municipal solid waste (MSW) management. The model is derived by an integration of interval linear programming, chance-constrained linear programming and mixed integer programming. This method is able to deal with multiple uncertainties associated with probability distributions and discrete intervals presented in both left hand side (LHS) and right hand side (RHS) of constraints and objective function in the model. The case study of Foshan city, China is used to examine the effectiveness of the model. The case study consists of three scenarios with different waste management policies. A trade – off between the system cost, the strictness of the policy and the level of constraints violation is analyzed. Generally, a gentler policy with high level of constraints violation leads to a lower system cost, and vice versa. The results obtained are useful for helping decision makers to figure out desired policy under various environmental and economic constraints.