An Internet-of-Things-Based Dynamic Scheduling Optimization Method for Unreliable Flexible Manufacturing Systems under Complex Operational Conditions

The dynamic scheduling problem (DSP) in unreliable flexible manufacturing systems (UFMSs) with concurrency, conflicts, resource sharing, and sequential operations is a complex optimization problem that requires the use of efficient solution methodologies. The effectiveness of scheduling UFMSs relies...

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Veröffentlicht in:Machines (Basel) Jg. 12; H. 3; S. 192
Hauptverfasser: Dabwan, Abdulmajeed, Kaid, Husam, Al-Ahmari, Abdulrahman, Alqahtani, Khaled N., Ameen, Wadea
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.03.2024
Schlagworte:
Cycle time
Decomposition
Downtime
Effectiveness
Flexible manufacturing systems
Genetic algorithms
Heuristic
Integer programming
Internet
Internet of Things
Linear equations
Linear programming
Mathematical models
Methods
Mixed integer
Modelling
Optimization
Petri net
Petri nets
Planning
Predictive maintenance
Production engineering
reliability
Scheduling
Simulation
Software
Germany
ISSN:2075-1702, 2075-1702
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Abstract The dynamic scheduling problem (DSP) in unreliable flexible manufacturing systems (UFMSs) with concurrency, conflicts, resource sharing, and sequential operations is a complex optimization problem that requires the use of efficient solution methodologies. The effectiveness of scheduling UFMSs relies on the quality of equipment maintenance. Currently, UFMSs with consistently large queues of parts awaiting service employ a repair-after-failure approach as a standard maintenance procedure. This method may require unexpected resources, incur costs, consume time, and potentially disrupt the operations of other UFMSs, either partially or fully. This study suggests using a predictive maintenance (PdM) strategy that utilizes the Internet of Things (IoT) to predict and avoid early mechanical equipment failures before they happen in UFMSs, thereby reducing unplanned downtime and enhancing reliability. Therefore, the objective of this paper is to construct timed Petri net (TPN) models using the IoT for the PdM configuration of mechanical equipment in the dynamic scheduling problem of UFMSs. This necessitates that users represent the specific problem using TPNs. The process of PN modeling requires the utilization of domain knowledge pertaining to the target problems as well as to machine information. However, it is important to note that the modeling rules for PNs are straightforward and limited in number. Consequently, the TPN model is applied to generate and formulate mixed-integer linear programming (MILP) instances accurately. This is done to identify the optimal production cycle time, which may be implemented in real-life scenarios. Several UFMS instances are used to demonstrate the applications and effectiveness of the proposed method. The computational results demonstrate that the proposed method shows superior solution quality, effectively solves instances for a total of 10 parts and 6 machines, and achieves a solution in a reasonable CPU time.
AbstractList The dynamic scheduling problem (DSP) in unreliable flexible manufacturing systems (UFMSs) with concurrency, conflicts, resource sharing, and sequential operations is a complex optimization problem that requires the use of efficient solution methodologies. The effectiveness of scheduling UFMSs relies on the quality of equipment maintenance. Currently, UFMSs with consistently large queues of parts awaiting service employ a repair-after-failure approach as a standard maintenance procedure. This method may require unexpected resources, incur costs, consume time, and potentially disrupt the operations of other UFMSs, either partially or fully. This study suggests using a predictive maintenance (PdM) strategy that utilizes the Internet of Things (IoT) to predict and avoid early mechanical equipment failures before they happen in UFMSs, thereby reducing unplanned downtime and enhancing reliability. Therefore, the objective of this paper is to construct timed Petri net (TPN) models using the IoT for the PdM configuration of mechanical equipment in the dynamic scheduling problem of UFMSs. This necessitates that users represent the specific problem using TPNs. The process of PN modeling requires the utilization of domain knowledge pertaining to the target problems as well as to machine information. However, it is important to note that the modeling rules for PNs are straightforward and limited in number. Consequently, the TPN model is applied to generate and formulate mixed-integer linear programming (MILP) instances accurately. This is done to identify the optimal production cycle time, which may be implemented in real-life scenarios. Several UFMS instances are used to demonstrate the applications and effectiveness of the proposed method. The computational results demonstrate that the proposed method shows superior solution quality, effectively solves instances for a total of 10 parts and 6 machines, and achieves a solution in a reasonable CPU time.
Audience Academic
Author Dabwan, Abdulmajeed
Al-Ahmari, Abdulrahman
Ameen, Wadea
Kaid, Husam
Alqahtani, Khaled N.
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CitedBy_id crossref_primary_10_1109_ACCESS_2025_3544772
crossref_primary_10_1080_0951192X_2024_2397822
crossref_primary_10_3390_machines13060519
crossref_primary_10_1007_s41872_025_00322_9
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Snippet The dynamic scheduling problem (DSP) in unreliable flexible manufacturing systems (UFMSs) with concurrency, conflicts, resource sharing, and sequential...
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SubjectTerms Cycle time
Decomposition
Downtime
Effectiveness
Flexible manufacturing systems
Genetic algorithms
Heuristic
Integer programming
Internet
Internet of Things
Linear equations
Linear programming
Mathematical models
Methods
Mixed integer
Modelling
Optimization
Petri net
Petri nets
Planning
Predictive maintenance
Production engineering
reliability
Scheduling
Simulation
Software
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Title An Internet-of-Things-Based Dynamic Scheduling Optimization Method for Unreliable Flexible Manufacturing Systems under Complex Operational Conditions
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