Research Article
BibTex RIS Cite

YAPAY SİNİR AĞLARI KULLANARAK ENERJİ TASARRUFLU ATÖLYE TİPİ ÇİZELGELEME

Year 2019, Volume: 28 Issue: 3, 143 - 154, 15.12.2019
https://doi.org/10.35379/cusosbil.644997

Abstract

Enerji
maliyetlerindeki artış ve iklimsel değişiklikler dolayısıyla, günümüzdeki
üretim işletmeleri geleneksel üretim süreçleri yerine sürdürülebilir üretim
süreçlerine geçmek zorunda kalmaktadırlar. Geleneksel çizelgeleme problemi
sadece işlem sürelerini dikkate alır ve enerji tasarrufu veya çevresel etkileri
dikkate almamaktadır. Bu çalışmada ise atölye tipi üretim için çizelgeleme
yapılırken harcanan en yüksek elektrik miktarı hesaplanarak, sadece toplam
işlerin bitiş süresinin minimizasyonu değil aynı zamanda elektrik tasarrufu
yapan bir çizelgeleme yapılmıştır. Her bir zaman dilimi için harcanan
elektrikler bulunmuş ve tüm işlemlerin bitiş zamanına kadar olan tüm zamanlar
için en büyük elektrik harcaması hesaplanmıştır. Bu değerin azaltılmaya
çalışılması ile elektrik tasarrufu sağlanmaya çalışılmıştır. Tamsayılı
matematiksel model oluşturularak yapay sinir ağları ile çözümler elde
edilmiştir. Uygulamada tüm işlemlerin bitiş süresinden biraz feragat edilerek
önemli ölçüde enerji tasarrufu yapıldığı görülmektedir.

References

  • Dai, M., Tang, D., Giret, A., Salido, M.A., & Li, W.D. (2013). Energy-efficent scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing, 29, 418 – 429.
  • Fang, K., Uhan, N, Zhao F., & Sutherland J. W., (2013). Flow shop scheduling with peak power consumption constraints. Annals of Operations Research, 206, 115 – 145.
  • Fang, K., Uhan, N., Zhao F., & Sutherland J. W., (2011). A new approach to scheduling in manufacturing for power comsumption and carbon footprint reduction. Journal of Manufacturing Systems, 30, 234 – 240.
  • Ku, W.,--Y., & Beck J. C., (2016). Mixed Integer Programming models for job shop scheduling: A computational analysis. Computer & Operations Research, 73, 165 – 173.
  • Liu, Y., Dong, H, Lohse, N., Petrovic S., & Gindy, N., (2014). An investigation into minimising total energy consumption and total weighted tardiness in job shops. Journal of Cleaner Production, 65, 87 – 96.
  • Luo, H., Du, B., Huang, G.Q., Chen, H., & Li, X., (2013). Hybrid flow shop scheduling consedering machine electricity consumption cost. International Journal of Production Economics, 146(2), 423 – 439.
  • May, G., Stahl, B., Taisch, M., & Prabhu, V., (2015). Multi-objective genetic algorithm for energy-efficient job shop scheduling. International Journal of Production Research, 53(23), 7071 – 7089.
  • Moon, J. Y., Shin, K., & Park, J. (2013). Optimizing of production scheduling with time-dependent and machine-dependent electricity cost for industrial energy efficiency. International Journal of Advanced Manufacturing Technologies, 68(1-4), 523 – 535.
  • Mouzon, G., & Yıldırım, M. B. (2008). A framework to minimise total energy consumption and total tardiness on a single machine. International Journal of Sustainable Engineering, 1(2), 211 – 230.
  • Mouzon, G., Yıldırım, M. B., & Twomey, J. (2007). Operational methods for minimising of energy consumption of manufacturing equipment. International Journal of Production Research, 45(18-19), 4247 – 4271.
  • Tang, D., Dai, M., Salido, M. A., & Giret, A. (2016). Energy-efficent dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Computers in Industry, 81, 82 – 95.
  • Shrouf, F., Ordieres-Mere J., Garcia-Sanchez A., & Ortega-Mier, M. (2014). Optimizing the production scheduling of a single machine to minimize total energy consumption costs. Journal of Cleaner Production, 67, 197 – 207.
  • Zhang, R., & Chiong, R. (2016). Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption. Journal of Cleaner Production, 112, 3361 – 3375.
  • Zhang, R., Zhao, F., Fang, K., & Sutherland J. W., (2014). Energy-conscious flow shop scheduling under time-of-use electricity tariffs. CIRP Annals-Manufacturing Technology, 63, 37 – 40.
Year 2019, Volume: 28 Issue: 3, 143 - 154, 15.12.2019
https://doi.org/10.35379/cusosbil.644997

Abstract

References

  • Dai, M., Tang, D., Giret, A., Salido, M.A., & Li, W.D. (2013). Energy-efficent scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing, 29, 418 – 429.
  • Fang, K., Uhan, N, Zhao F., & Sutherland J. W., (2013). Flow shop scheduling with peak power consumption constraints. Annals of Operations Research, 206, 115 – 145.
  • Fang, K., Uhan, N., Zhao F., & Sutherland J. W., (2011). A new approach to scheduling in manufacturing for power comsumption and carbon footprint reduction. Journal of Manufacturing Systems, 30, 234 – 240.
  • Ku, W.,--Y., & Beck J. C., (2016). Mixed Integer Programming models for job shop scheduling: A computational analysis. Computer & Operations Research, 73, 165 – 173.
  • Liu, Y., Dong, H, Lohse, N., Petrovic S., & Gindy, N., (2014). An investigation into minimising total energy consumption and total weighted tardiness in job shops. Journal of Cleaner Production, 65, 87 – 96.
  • Luo, H., Du, B., Huang, G.Q., Chen, H., & Li, X., (2013). Hybrid flow shop scheduling consedering machine electricity consumption cost. International Journal of Production Economics, 146(2), 423 – 439.
  • May, G., Stahl, B., Taisch, M., & Prabhu, V., (2015). Multi-objective genetic algorithm for energy-efficient job shop scheduling. International Journal of Production Research, 53(23), 7071 – 7089.
  • Moon, J. Y., Shin, K., & Park, J. (2013). Optimizing of production scheduling with time-dependent and machine-dependent electricity cost for industrial energy efficiency. International Journal of Advanced Manufacturing Technologies, 68(1-4), 523 – 535.
  • Mouzon, G., & Yıldırım, M. B. (2008). A framework to minimise total energy consumption and total tardiness on a single machine. International Journal of Sustainable Engineering, 1(2), 211 – 230.
  • Mouzon, G., Yıldırım, M. B., & Twomey, J. (2007). Operational methods for minimising of energy consumption of manufacturing equipment. International Journal of Production Research, 45(18-19), 4247 – 4271.
  • Tang, D., Dai, M., Salido, M. A., & Giret, A. (2016). Energy-efficent dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Computers in Industry, 81, 82 – 95.
  • Shrouf, F., Ordieres-Mere J., Garcia-Sanchez A., & Ortega-Mier, M. (2014). Optimizing the production scheduling of a single machine to minimize total energy consumption costs. Journal of Cleaner Production, 67, 197 – 207.
  • Zhang, R., & Chiong, R. (2016). Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption. Journal of Cleaner Production, 112, 3361 – 3375.
  • Zhang, R., Zhao, F., Fang, K., & Sutherland J. W., (2014). Energy-conscious flow shop scheduling under time-of-use electricity tariffs. CIRP Annals-Manufacturing Technology, 63, 37 – 40.
There are 14 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Mert Demircioğlu 0000-0002-2287-2067

Publication Date December 15, 2019
Submission Date November 10, 2019
Published in Issue Year 2019 Volume: 28 Issue: 3

Cite

APA Demircioğlu, M. (2019). YAPAY SİNİR AĞLARI KULLANARAK ENERJİ TASARRUFLU ATÖLYE TİPİ ÇİZELGELEME. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 28(3), 143-154. https://doi.org/10.35379/cusosbil.644997