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This chapter proposes a genetic algorithm (GA)-based approach as an all-purpose problem-solving method for optimization problems with uncertainty. This chapter explains the GA-based method and presents details on the computation procedures involved for solving the three types of inexact optimization problems, which include the ILP, inexact quadratic programming (IQP) and inexact nonlinear programming (INLP) optimization problems. In the three-stage GA-based method for solution of ILP problems, also called GAILP, the upper and lower bounds of the inexact numbers of coefficients can be calculated directly without any uncertainty in the coefficients by substituting the initial subopti‐ mal decision variables into the objective function. The GAILP has been extended to solve the IQP problems and the more complicated INLP problems. The implementation of these approaches was performed using the Genetic Algorithm Solver of MATLAB. The proposed GA-based approaches were applied for management of a set of case scenarios related to municipal solid waste management. A comparison of the results generated by the proposed GA-based optimization approach with those produced by the traditional interactive binary analysis method reveals that the proposed approach has fewer limitations and involves less complex procedures in solving the inexact optimization problems.

Genetic Algorithm-Based Approaches for Solving Inexact Optimization Problems and their Applications for Municipal Solid Waste Management