The pressure from today’s economic and energy markets demands further distribution of decision making in large, dynamic networks. To this end, distributed model predictive control (MPC) divides the task of operating a dynamic network into a set of small, localized subtasks, one for each distributed control agent. To guide the decomposition of the overall task, which plays a central role in the quality of the operation yielded by the distributed agents, this paper proposes a model for problem decomposition based on the design of agent communication networks. The model gives rise to the communication-network design problem: A bicriteria optimization problem whose objectives are the maximization of the influence perceived by the agents and the minimization of the communication cost. The benefit of the model is the potential to concentrate the effort of implementing distributed MPC, and measuring its actual performance, on a reduced number of decompositions, namely those that are Pareto efficient for the model. The paper gives an account of related work, including the computational complexity of finding Pareto efficient solutions, an integer programming formulation, and families of valid inequalities. Its main contribution is the demonstration that the model can be effective, meaning that Pareto efficient solutions to the model tend to induce efficient problem decompositions. The experimental evidence was gathered by applying the model to decompose control problems of two representative networks, namely arrays of pendulums and electric power networks, and measuring the quality of the operation delivered by the distributed control agents.

In this paper, we continue the dialog started by Roth [19] between economics and engineering in the context of design of markets. We take the position that markets and engineered artifacts are thee the products of a social process of design formulation. Our perspective is that designing of markets and artifacts follow the same kind of problem formulation and solution testing process. Further, we show through two case studies, that the design of engineered artifacts and markets are often interdependent. In light of their similarities, methodologies for modeling and testing using theoretical, analytical, computation and physical models in engineering can inform the development of methods for testing in the design of markets. We also illustrate by example devising of a test and results of its execution thorough the use of a simple computational model with the required salient features of an electric power market. We conclude by summarizing the similarities between the designing of markets and artifacts and call for a continued dialogue between engineers and economists.

The Engineering Design Research Center (EDRC) promotes the establishment and dissemination of a scientific basis for design based on an interdisciplinary approach to researrh and education. The Center's vision is that a scientific framework for design can exploit rapid advances in computer and communications technologies to serve the competitive need for reduced product development cycles, improved quality and reliability, and lower cost. Basic notions of the product cycle underlie the Center's strategic plan, a framework of researrh thrusts leading from barriers in design science to goals and vision. As an NSF Engineering Research Center, EDRC conducts cross-educational and industrial programs in parallel with research

The real-time operations of electric power networks are subject to two sets of forces. The first, including deregulation movements and growing environmental concerns, is acting to increase the complexity of operations. The second, including new computer technologies and emerging knowledge-based agents, provides some means for handling additional complexity. The authors argue that organizational changes will have to be made before the second set of forces can be applied to effectively counter the first. To make this argument, a framework for discussing organizational structures is presented. Then the structures of the two generations of computer-based, multiagent systems that have been developed for operations are reviewed. It is pointed out that these structures are well-suited to the algorithmic tasks involved in operations but not to the knowledge-based tasks. The authors conclude with some suggestions for research into alternative structures.>

The paper describes a technique for estimating the minimum execution time of an algorithm or a mix of algorithms on a distributed processing system. Bottlenecks that would have to be removed to further reduce the execution time are identified. The main applications are for the high level design of special purpose distributed processing systems.