Decision making and cooperative interaction via coupling agents in organizationally distributed systems

This paper develops a methodology for decision-making in organizationally distributed systems where decision authorities and information are dispersed in multiple organizations. Global performance is achieved through cooperative interaction and partial information sharing among organizations. The information shared among organizations is contrived using modified Lagrangian relaxation techniques. Novel to the methodology is that no single master problem with a global view of the system is required to guide the decision process. Rather, multiple artificial decision entities, termed Coupling Agents, are associated with subsets of coupling constraints. The proposed generic model can be applied to decision-making problems with a variety of mathematical structures. In this paper the methodology is applied to parameter design problems to illustrate the behavior of the proposed methodology in the realm of nonlinear optimization.