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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| Veröffentlicht in: | Information sciences Jg. 329; S. 491 - 502 |
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| Abstract | •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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| AbstractList | 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. •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. |
| Author | Takahashi, Ricardo H.C. Silva, Regiane S. Costa, Tatiana A. Pena, Patrícia N. |
| Author_xml | – sequence: 1 givenname: Patrícia N. surname: Pena fullname: Pena, Patrícia N. email: ppena@ufmg.br organization: Department of Electronics Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil – sequence: 2 givenname: Tatiana A. surname: Costa fullname: Costa, Tatiana A. email: tatiana@decea.ufop.br organization: Department of Exact and Applied Sciences, Universidade Federal de Ouro Preto, João Monlevade, MG, Brazil – sequence: 3 givenname: Regiane S. surname: Silva fullname: Silva, Regiane S. email: regiane.ufop@gmail.com organization: Department of Automation and Control Engineering and Fundamental Techniques, Universidade Federal de Ouro Preto, Ouro Preto, MG, Brazil – sequence: 4 givenname: Ricardo H.C. surname: Takahashi fullname: Takahashi, Ricardo H.C. email: taka@mat.ufmg.br organization: Department of Mathematics, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil |
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| Snippet | •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... A new approach for the problem of optimal task scheduling in flexible manufacturing systems is proposed in this work, as a combination of metaheuristic... |
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| SubjectTerms | Control systems Discrete Event Systems Encoding Flexible manufacturing systems Heuristic methods Metaheuristics Optimization Schedules Scheduling Searching Supervisory control Supervisory Control Theory |
| Title | Control of Flexible Manufacturing Systems under model uncertainty using Supervisory Control Theory and evolutionary computation schedule synthesis |
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