Control of flexible manufacturing systems under model uncertainty using Supervisory Control Theory and evolutionary computation schedule synthesis

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dc.creatorPatrícia N. Pena
dc.creatorTatiana A. Costa
dc.creatorRegiane S. Silva
dc.creatorRicardo Hiroshi Caldeira Takahashi
dc.date.accessioned2025-03-25T16:25:42Z
dc.date.accessioned2025-09-09T00:56:01Z
dc.date.available2025-03-25T16:25:42Z
dc.date.issued2016
dc.identifier.doi10.1016/j.ins.2015.08.056
dc.identifier.issn0020-0255
dc.identifier.urihttps://hdl.handle.net/1843/80910
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofInformation Sciences
dc.rightsAcesso Restrito
dc.subjectEngenharia de produção
dc.subjectAdministração da produção
dc.subject.othertask scheduling in manufacturing
dc.subject.othermetaheuristics techniques , Supervisory Control of Discrete Event Systems
dc.subject.otherdiscrete event dynamical systems
dc.subject.othersupervisory control
dc.subject.otheruncertainties and plant disturbance effects
dc.titleControl of flexible manufacturing systems under model uncertainty using Supervisory Control Theory and evolutionary computation schedule synthesis
dc.typeArtigo de periódico
local.citation.epage502
local.citation.issue1
local.citation.spage491
local.citation.volume329
local.description.resumoA new approach for the problem of optimal task scheduling in flexible manufacturing systems is proposed in this work, as a combination of metaheuristic optimization techniques with the supervisory control theory of discrete-event systems. A specific encoding, the word-shuffling encoding, which avoids the generation of a large number of infeasible sequences, is employed. A metaheuristic method based on a Variable Neighborhood Search is then built using such an encoding. The optimization algorithm performs the search for the optimal schedules, while the supervisory control has the role of codifying all the problem constraints, allowing an efficient feasibility correction procedure, and avoiding schedules that are sensitive to uncertainties in the execution times associated with the plant operation. In this way, the proposed methodology achieves a system performance which is typical from model-predictive scheduling, combined with the robustness which is required from a structural control.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.departmentICX - DEPARTAMENTO DE MATEMÁTICA
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S002002551500691X

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