Distributed Large-scale Mixed-Integer Optimization with Application to Energy and Multi-robot Networks

Camisa, Andrea (2021) Distributed Large-scale Mixed-Integer Optimization with Application to Energy and Multi-robot Networks, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Ingegneria biomedica, elettrica e dei sistemi, 33 Ciclo.
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Several decision and control tasks in cyber-physical networks can be formulated as large- scale optimization problems with coupling constraints. In these "constraint-coupled" problems, each agent is associated to a local decision variable, subject to individual constraints. This thesis explores the use of primal decomposition techniques to develop tailored distributed algorithms for this challenging set-up over graphs. We first develop a distributed scheme for convex problems over random time-varying graphs with non-uniform edge probabilities. The approach is then extended to unknown cost functions estimated online. Subsequently, we consider Mixed-Integer Linear Programs (MILPs), which are of great interest in smart grid control and cooperative robotics. We propose a distributed methodological framework to compute a feasible solution to the original MILP, with guaranteed suboptimality bounds, and extend it to general nonconvex problems. Monte Carlo simulations highlight that the approach represents a substantial breakthrough with respect to the state of the art, thus representing a valuable solution for new toolboxes addressing large-scale MILPs. We then propose a distributed Benders decomposition algorithm for asynchronous unreliable networks. The framework has been then used as starting point to develop distributed methodologies for a microgrid optimal control scenario. We develop an ad-hoc distributed strategy for a stochastic set-up with renewable energy sources, and show a case study with samples generated using Generative Adversarial Networks (GANs). We then introduce a software toolbox named ChoiRbot, based on the novel Robot Operating System 2, and show how it facilitates simulations and experiments in distributed multi-robot scenarios. Finally, we consider a Pickup-and-Delivery Vehicle Routing Problem for which we design a distributed method inspired to the approach of general MILPs, and show the efficacy through simulations and experiments in ChoiRbot with ground and aerial robots.

Tipologia del documento
Tesi di dottorato
Camisa, Andrea
Dottorato di ricerca
Settore disciplinare
Settore concorsuale
Parole chiave
Distributed Optimization, Primal Decomposition, Constraint-coupled Optimization, Time-varying Networks, Mixed-integer Linear Programming (MILP), Distributed Microgrid Control, Generative Adversarial Networks (GANs), Cooperative Robotics, Robot Operating System 2, Distributed Pickup and Delivery
Data di discussione
8 Giugno 2021

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