Distributed and Asynchronous Coordination of a Mixed-Integer Linear System via Surrogate Lagrangian Relaxation

With the emergence of the Internet of Things that allows communications and local computations and with the vision of Industry 4.0, a foreseeable transition is from centralized system planning and operation toward decentralization with interacting components and subsystems, e.g., self-optimizing fac...

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Vydané v:IEEE transactions on automation science and engineering Ročník 18; číslo 3; s. 1191 - 1205
Hlavní autori: Bragin, Mikhail A., Yan, Bing, Luh, Peter B.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Communication
Convergence
Coordination
Distributed and asynchronous algorithms
Factories
Integer programming
Internet of Things
Lagrangian functions
Linear programming
Lyapunov methods
Mixed integer
Mixed integer linear programming
mixed-integer linear programming (MILP) problems
Multipliers
Optimization
Paving
Relaxation methods
self-optimizing factories
Subsystems
surrogate Lagrangian relaxation (LR)
ISSN:1545-5955, 1558-3783
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Shrnutí:With the emergence of the Internet of Things that allows communications and local computations and with the vision of Industry 4.0, a foreseeable transition is from centralized system planning and operation toward decentralization with interacting components and subsystems, e.g., self-optimizing factories. In this article, a new "price-based" decomposition and coordination methodology is developed to efficiently coordinate a system consisting of distributed subsystems such as machines and parts, which are described by mixed-integer linear programming (MILP) formulations, in an asynchronous way. The novel method is a dual approach, whereby the coordination is performed by updating Lagrangian multipliers based on economic principles of "supply and demand." To ensure low communication requirements within the method, exchanges between the "coordinator" and subsystems are limited to "prices" (Lagrangian multipliers) broadcast by the coordinator and to subsystem solutions sent at the coordinator. Asynchronous coordination, however, may lead to convergence difficulties since the order in which subsystem solutions arrive at the coordinator is not predefined as a result of uncertainties in communication and solving times. Under realistic assumptions of finite communication and solve times, the convergence of our method is proven by innovatively extending the Lyapunov stability theory. Numerical testing of generalized assignment problems through simulation demonstrates that the method converges fast and provides near-optimal results, paving the way for self-optimizing factories in the future. Accompanying CPLEX codes and data are included. Note to Practitioners -In view of a foreseeable transition toward self-optimizing factories whereby machines and parts have communication and computational capabilities, a novel "price-based" distributed and asynchronous method to coordinate a system consisting of distributed subsystems is developed. Under realistic assumptions of finite communication and solve times, method convergence is proven. Numerical testing of generalized assignment problems through simulation demonstrates that the method converges fast and provides near-optimal results, paving the way for self-optimizing factories in the future. Accompanying CPLEX codes and data are included.
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ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2020.2998048