Constrained optimization is a challenging branch of operationsresearch that aims to create a model which has a wide range ofapplications in the supply chain, telecommunications and medicalfields. As the problem structure is split into two main components,the objective is to accomplish the feasible set framed by thesystem constraints. The aim of this book is expose optimizationproblems that can be expressed as graphs, by detailing, for eachstudied problem, the set of nodes and the set of edges. Thisgraph modeling is an incentive for designing a platform thatintegrates all optimization components in order to output the bestsolution regarding the parameters' tuning. The authors propose intheir analysis, for optimization problems, to provide theirgraphical modeling and mathematical formulation and expose some oftheir variants. As a solution approaches, an optimizer can be themost promising direction for limited-size instances. For largeproblem instances, approximate algorithms are the most appropriateway for generating high quality solutions. The authors thuspropose, for each studied problem, a greedy algorithm as aproblem-specific heuristic and a genetic algorithm as ametaheuristic.