The aim of this master thesis has been to present several network optimization techniques with an emphasis on Lagrangean relaxation, Dantzig-Wolfe decomposition and Benders decomposition. These techniques reduce problems that we cannot solve using classical algorithms to classical problems. We have introduced a non-linear problem with an almost network structure and we have provided a few examples of such problems that are often used in practice. Lagrangean relaxation is one of the most commonly used solving techniques in mathematical programming which allows us to approach a difficult optimization problem with a simpler and more relaxed problem. Dantzig-Wolfe decomposition can be used when a linear optimization problem has special structure to generate subproblems that can be solved efficiently. Benders decomposition is a broadly used exact solution method for stochastic programs and it has been increasingly applied to transportation and logistics planning problems under uncertainty. Finally, we have applied Lagrange’s relaxation to the uncapacitated fixed charge facility location problems and Benders’ decomposition to the capacitated fixed charge facility location problems.