In this study, we analyze solution methods for approximating the Pareto front of bi-objective mixed-integer linear programming problems. First of all, we discuss a two-stage evolutionary algorithm. Given the values for the integer variables, the second stage of the two-stage evolution algorithm generates the values for the continuous variables of the corresponding Pareto efficient solutions. Then, the corresponding Pareto efficient solutions of integer variables are compared in the first-stage of the two-stage evolutionary algorithm to determine the Pareto efficient integer solutions. These stages are repeated within an evolutionary heuristic structure to approximate the Pareto front. Secondly, we propose a decomposition approach to separate the integral part of the feasible region of the problem. The decomposition approach separates the problem into sub-problems, each of which has an additional constraint, and approximates the Pareto fronts of the sub-problems using the two-stage evolutionary algorithm discussed. Then, using the sub-problem Pareto fronts, the Pareto front of the main problem is approximated. A numerical study is conducted to compare the two-stage evolutionary algorithm with the decomposition approach, which uses the two-stage evolutionary algorithm
