Affiliations: [a] School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| [b] Department of Mathematics, Computer Science, and Physics, University of Udine, Udine, Italy
| [c] Department of Computer Science, New Mexico State University, Las Cruces, NM, USA
Correspondence:
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Corresponding author: Ferdinando Fioretto, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA. E-mail: fioretto@gatech.edu.
Abstract: Distributed Constraint Optimization Problems (DCOPs) have emerged as one of the prominent multi-agent architectures to govern the agents’ autonomous behavior in a cooperative multi-agent system (MAS) where several agents coordinate with each other to optimize a global cost function taking into account their local preferences. They represent a powerful approach to the description and resolution of many practical problems. However, typical MAS applications are characterized by complex dynamics and interactions among a large number of entities, which translate into hard combinatorial problems, posing significant challenges from a computational and coordination standpoints. This paper reviews two methods to promote a hierarchical parallel model for solving DCOPs, with the aim of improving the performance of the DCOP algorithm. The first is a Multi-Variable Agent (MVA) DCOP decomposition, which exploits co-locality of an agent’s variables allowing the adoption of efficient centralized techniques to solve the subproblem of an agent. The second is the use of Graphics Processing Units (GPUs) to speed up a class of DCOP algorithms. Finally, exploiting these hierarchical parallel model, the paper presents two critical applications of DCOPs for demand response (DR) program in smart grids. The Multi-agent Economic Dispatch with Demand Response (EDDR), which provides an integrated approach to the economic dispatch and the DR model for power systems, and the Smart Home Device Scheduling (SHDS) problem, that formalizes the device scheduling and coordination problem across multiple smart homes to reduce energy peaks.
