In the proposed thesis, we study Distributed Constraint Optimization Problems (DCOPs), which are problems where several agents coordinate with each other to optimize a global cost function. The use of DCOPs has gained momentum, due to their capability of addressing complex and naturally distributed problems. However, the adoption of DCOP on large problems faces two main limitations: (I) Modeling limitations, as current resolution methods detach the model from the resolution process, assuming that each agent controls a single variable of the problem; and (2) Solving capabilities, as the inability of current approaches to capitalize on the presence of structural information which may allow incoherent/unnecessary data to reticulate among the agents as well as to exploit structure of the agent's local problems. The purpose of the proposed dissertation is to address such limitations, studying how to adapt and integrate insights gained from centralized solving techniques in order to enhance DCOP performance and scalability, enabling their use for the resolution of real-world complex problems. To do so, we hypothesize that one can exploit the DCOP structure in both problem modeling and problem resolution phases.