Dergiler / ACADEMIC PLATFORM-JOURNAL OF ENGINEERING AND SCIENCE / 2021 / Cilt: 9 - Sayı: 2

Scheduling Optimization in Automotive Supplier Industry under Sequence Dependent Constraints

Sayfa
378–387
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

It has been clarified that if the developed algorithm is applied to all machines in the department, productivity and product quality will be increased by saving waste materials.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In today's production conditions, it is very important for companies to produce products that meet the determined quality standards.
📊
Temel Bulgular & Veri:The algorithm was run using data from two machines out of 18, saving 134 kg of waste materials.
💡
Sonuç & Katkı:It has been clarified that if the developed algorithm is applied to all machines in the department, productivity and product quality will be increased by saving waste materials.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Özet

In today's production conditions, it is very important for companies to produce products that meet the determined quality standards. In addition to the production of semi-finished products produced in plastic injection lines with high quality and efficiency, it is aimed to reduce the lost time and waste materials in production. Since automobile headlights can be in many different colors, it is necessary to keep the product quality at a high level in the transition between colors in the production made by plastic injection and to reduce the number of defective products caused by color errors. In this study, the problem of a single row-dependent machine to reduce the amount of waste that occurs between color transitions in the plastic injection line of an automotive side-industry company producing headlights is discussed. Tabu Search method, which is one of the heuristic optimization algorithms was used to solve the problem. In the solution, production sequences that will reduce the amount of waste of the products in the production program were created by using the annual data kept by the production department of the factory. The algorithm was run using data from two machines out of 18, saving 134 kg of waste materials. It has been clarified that if the developed algorithm is applied to all machines in the department, productivity and product quality will be increased by saving waste materials.

🔖

Bu Makaleye Atıf Yap

MOHAMEDSALİH, M., ŞİMŞİR, F. (2021). Scheduling Optimization in Automotive Supplier Industry under Sequence Dependent Constraints. ACADEMIC PLATFORM-JOURNAL OF ENGINEERING AND SCIENCE, Cilt: 9 - Sayı: 2, 378–387. https://ajindex.com/articles/354802

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (11)

  1. T. Saraç and S. Kaya, “Plasti̇ k enjeksi̇ yon maki̇ neleri̇ ni̇ n vardi̇ ya bazinda çi̇ zelgelenmesi̇ problemi̇ i̇ çi̇ n bi̇ r hedef programlama modeli̇,” Journal of Industrial Engineering, vol. 24, pp. 12–26
  2. T. Eren and E. Güner, “Sira-Bağimli Hazirlik Zamanli İki Ölçütlü Çizelgeleme Problemi: Toplam Tamamlanma Zamani Ve Maksimum Erken Bitirme,” Erciyes University Journal of Science and Technology, vol. 23, no. 1-2, pp. 95–105, 2007
  3. T. Eren, “Learning and Deteriorating Effects on the Single Machine Scheduling Problems,” International Journal of Engineering Research and Development, vol. 6, no. 1, pp. 15–20, Jan. 2014. [4] "Digital Conversion on the Way to Industry 4.0", Springer Science and Business Media LLC, 2021 [5] V.F. Viagas, A. Costa, Two novel population based algorithms for the single machine scheduling problem with sequence dependent setup times and release times, Swarm and Evolutionary Computation, Volume 63, 2021, 100869, ISSN 2210-6502,
  4. Y. Zhao and G. Wang, “A dynamic differential evolution algorithm for the dynamic single-machine scheduling problem with sequence-dependent setup times Yue,” Journal of the Operational Research Society, vol. 71, no. 2, pp. 225–236, 2020
  5. S. Muştu and T. Eren, “The Single Machine Scheduling Problem with Sequence-Dependent Setup Times and a Learning Effect on Processing Times.,” Applied Soft Computing, vol. 71, pp. 291–306, 2018
  6. P. Perez-Gonzalez and J. M. Framinan, “Single machine scheduling with periodic machine availability,” Computers & Industrial Engineering, vol. 123, pp. 180–188, 2018
  7. R. M. Souza, S. Ricardo, and M. Felizardo, “Algorithms for job scheduling problems with distinct time windows and general earliness/tardiness penalties,” Computers and Operations Research, vol. 81, pp. 203–215, 2017
  8. Z. Ceylan, E. Karan, Ç. Bakirci, and S. Sabuncu “Single Machine Scheduling Problem with Sequence Dependent Setup Times: An Application in White Goods I,” International Journal of Multidisciplinary Studies and Innovative Technologies, vol. 3, no. 1, pp. 14–21, 2019
  9. O. Herr and A. Goel, “Minimising total tardiness for a single machine scheduling problem with family setups and resource constraints,” European Journal of Operational Research, vol. 248, no. 1, pp. 123–135, 2016
  10. T. Prabhakar, “A Production Scheduling Problem with Sequencing,” Management Science, vol. 21, no. 1, pp. 34–42, 2014
  11. T. C. E. Cheng, W. C. Lee,