A Robust Optimization Approach to Network Control Using Local Information Exchange

Georgios Darivianakis (), Angelos Georghiou (), Soroosh Shafiee () and John Lygeros ()
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Georgios Darivianakis: Alpiq AG, 4601 Olten, Switzerland
Angelos Georghiou: Department of Business and Public Administration, University of Cyprus, Nicosia 1678, Cyprus
Soroosh Shafiee: Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853
John Lygeros: Automatic Control Laboratory, ETH Zurich, 8092 Zurich, Switzerland

Operations Research, 2025, vol. 73, issue 5, 2849-2866

Abstract: Designing policies for a network of agents is typically done by formulating an optimization problem where each agent has access to state measurements of all the other agents in the network. Such policy designs with centralized information exchange result in optimization problems that are typically hard to solve, require establishing substantial communication links, and do not promote privacy since all information is shared among the agents. Designing policies based on arbitrary communication structures can lead to nonconvex optimization problems that are typically NP-hard. In this work, we propose an optimization framework for decentralized policy designs. In contrast to the centralized information exchange, our approach requires only local communication exchange among the neighboring agents matching the physical coupling of the network. Thus, each agent only requires information from its direct neighbors, minimizing the need for excessive communication and promoting privacy amongst the agents. Using robust optimization techniques, we formulate a convex optimization problem with a loosely coupled structure that can be solved efficiently. We numerically demonstrate the efficacy of the proposed approach in energy management and supply chain applications. We show that the proposed approach leads to solutions that closely approximate those obtained by the centralized formulation only at a fraction of the computational effort.

Keywords: Optimization; robust optimization; network control; decentralized policy design; local information; state forecast sets; decision rules (search for similar items in EconPapers)
Date: 2025
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