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Comparative Study of Application of Production Sequencing and Scheduling Problems in Tire Mixing Operations with ADAM, Grey Wolf Optimizer, and Genetic Algorithm

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Pubblicato in:Systems vol. 13, no. 11 (2025), p. 998-1024
Autore principale: Yıldırım Elif
Altri autori: Denizhan Berrin
Pubblicazione:
MDPI AG
Soggetti:
Scheduling
Process planning
Comparative studies
Machine learning
Performance measurement
Deep learning
Genetic algorithms
Assignment problem
Optimization techniques
Production scheduling
Optimization
Flexibility
Effectiveness
Approximation
Flexible manufacturing systems
Resource utilization
Manufacturing
Efficiency
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Abstract:Scheduling and sequencing problems in manufacturing are complex and challenging to solve. Effective process planning is fundamental to optimizing production time and resource utilization in process-type manufacturing environments such as tire manufacturing. This research focuses on an existing tire manufacturing process. The scheduling problem in the compound mixing stage, which is considered the most challenging and vital stage of tire manufacturing, has been solved in this study. Adaptive Moment Estimation Optimizer (ADAM Optimizer), Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) are selected as solution methodologies. A comparative analysis is performed to evaluate the effectiveness of these algorithms based on critical performance metrics, including completion times, machine utilization, and setup numbers. The results of this study show that ADAM and algorithmic methods optimize machine utilization by 1.28% and save 32.6% production time, outperforming the traditional manual allocation strategies mainly used by industrial companies, as well as GWO and GA.
ISSN:2079-8954
DOI:10.3390/systems13110998
Fonte:Advanced Technologies & Aerospace Database