A multi-objective approach for manufacturing systems with multiple production routes based on supervisory control theory and heuristic algorithms

Heterogeneity among equipment in industrial production lines may have a major impact on energy consumption and makespan. The Supervisory Control Theory of discrete event systems proved to be especially useful to model the production system respecting its constraints and ensuring the safe execution o...

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Bibliographic Details
Published in:Discrete event dynamic systems 2023-12, Vol.33 (4), p.373-394
Main Authors: Alves, Lucas V. R., Rafael, Gustavo C., Batista, Lucas S., Pena, Patrícia N.
Format: Article
Language:English
Subjects:
Algorithms
Constraint modelling
Control
Control equipment
Control theory
Convex and Discrete Geometry
Discrete event systems
Electrical Engineering
Energy consumption
Heterogeneity
Heuristic methods
Machines
Manufacturing
Mathematics
Mathematics and Statistics
Multiple criterion
Multiple objective analysis
Operations Research/Decision Theory
Processes
Production lines
Supervisory control
Systems Theory
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Summary:Heterogeneity among equipment in industrial production lines may have a major impact on energy consumption and makespan. The Supervisory Control Theory of discrete event systems proved to be especially useful to model the production system respecting its constraints and ensuring the safe execution on a real plant, such that an optimized production plan can be picked among the safe sequences. In this work, the makespan and the energy consumption minimization problem in discrete event systems with multiple production routes is addressed as a multi-objective problem. A multi-objective modeling is proposed, which considers the information regarding the power demanded by the system in each state of each equipment and the expected duration of each operation in the system. A dedicated Multi-Objective Variable Neighborhood Search (MOVNS) algorithm is also proposed to estimate an adequate set of trade-off solutions. A multi-criteria decision aid method is used to support the selection of an appropriate solution for the problem. The combination of the proposed modeling with both the MOVNS and a decision-making support has shown to be efficient regarding the solution of four test instances considered in this work.
ISSN:0924-6703
1573-7594
DOI:10.1007/s10626-023-00379-7