Comparative Study of Application of Production Sequencing and Scheduling Problems in Tire Mixing Operations with ADAM, Grey Wolf Optimizer, and Genetic Algorithm
সংরক্ষণ করুন:
| প্রকাশিত: | Systems vol. 13, no. 11 (2025), p. 998-1024 |
|---|---|
| প্রধান লেখক: | |
| অন্যান্য লেখক: | |
| প্রকাশিত: |
MDPI AG
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| ট্যাগগুলো: |
কোনো ট্যাগ নেই, প্রথমজন হিসাবে ট্যাগ করুন!
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MARC
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| 022 | |a 2079-8954 | ||
| 024 | 7 | |a 10.3390/systems13110998 |2 doi | |
| 035 | |a 3275563915 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231636 |2 nlm | ||
| 100 | 1 | |a Yıldırım Elif | |
| 245 | 1 | |a Comparative Study of Application of Production Sequencing and Scheduling Problems in Tire Mixing Operations with ADAM, Grey Wolf Optimizer, and Genetic Algorithm | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Scheduling | ||
| 653 | |a Process planning | ||
| 653 | |a Comparative studies | ||
| 653 | |a Machine learning | ||
| 653 | |a Performance measurement | ||
| 653 | |a Deep learning | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Assignment problem | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Production scheduling | ||
| 653 | |a Optimization | ||
| 653 | |a Flexibility | ||
| 653 | |a Effectiveness | ||
| 653 | |a Approximation | ||
| 653 | |a Flexible manufacturing systems | ||
| 653 | |a Resource utilization | ||
| 653 | |a Manufacturing | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Denizhan Berrin | |
| 773 | 0 | |t Systems |g vol. 13, no. 11 (2025), p. 998-1024 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275563915/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3275563915/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275563915/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |