Control of Flexible Manufacturing Systems under model uncertainty using Supervisory Control Theory and evolutionary computation schedule synthesis

•The problem of task scheduling in manufacturing systems is tackled.•An extension of the Supervisory Control of Discrete Event Systems is used to model the constraints.•Metaheuristic computation techniques are used to find the optimal schedule that minimizes the makespan.•The methodology shows to be...

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Bibliographic Details
Published in:Information sciences Vol. 329; pp. 491 - 502
Main Authors: Pena, Patrícia N., Costa, Tatiana A., Silva, Regiane S., Takahashi, Ricardo H.C.
Format: Journal Article
Language:English
Published: Elsevier Inc 01.02.2016
Subjects:
Control systems
Discrete Event Systems
Encoding
Flexible manufacturing systems
Heuristic methods
Metaheuristics
Optimization
Schedules
Scheduling
Searching
Supervisory control
Supervisory Control Theory
Supervisory Control Theory
Discrete Event Systems
Scheduling
Metaheuristics
Optimization
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:•The problem of task scheduling in manufacturing systems is tackled.•An extension of the Supervisory Control of Discrete Event Systems is used to model the constraints.•Metaheuristic computation techniques are used to find the optimal schedule that minimizes the makespan.•The methodology shows to be robust to uncertainties and plant disturbance effects. A 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.
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ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2015.08.056