SavedAdd your journal

Journals / Kapadokya Akademik Bakış / 2017 / Cilt: 1 - Sayı: 1

USING PARALLEL KANGAROO ALGORITHM TO SOLVE FLOW SHOP SCHEDULING PROBLEMS WITH FUZZY LEARNING EFFECT

Bulanık Öğrenme Etkili Akış Tipi Çizelgeleme Problemlerinin Paralel Kanguru Algoritması İle Çözümü

Ahmet Sezer KÜPELİ
Orhan ENGİN
Batuhan Eren ENGİN
Pages
31–48
Publication Date
2017-06-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The study is concluded with comparing the results found in other studies.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Knowing that the scheduling problems considering learning ability can give more realistic results, this paper deals with flow shop scheduling problem considering learning effects due to the human factor.
📊
Key Findings & Data:Uncertain processing time is frequently encountered in scheduling problems; therefore, the processing times are fuzzed.
💡
Conclusion & Impact:The study is concluded with comparing the results found in other studies.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Akış tipi çizelgeleme · Bulanık mantık · Öğrenme etkisi

Abstract

Knowing that the scheduling problems considering learning ability can give more realistic results, this paper deals with flow shop scheduling problem considering learning effects due to the human factor. Uncertain processing time is frequently encountered in scheduling problems; therefore, the processing times are fuzzed. Fuzzy logic is a frequently used method to solve this problem in the literature. Parallel Kangaroo Algorithm is used as a solution method. The study is concluded with comparing the results found in other studies.

Özet

Öğrenme etkili çizelgeleme problemlerinin daha gerçekçi sonuçlar verdiği bilinmektedir. Bu çalışmada, insan faktöründen kaynaklanan öğrenme etkisini göz önüne alan akış tipi çizelgeleme problemleri ele alınmıştır. Belirsiz işlem sürelerine çizelgeleme problemlerinde sıklıkla karşılaşılır. Bu nedenle işlem süreleri bulanıklaştırılmıştır. Bulanık mantık literatürde bu problemlerin çözümü için sıkça kullanılan bir yöntemdir. Paralel Kanguru Algoritması çözüm metodu olarak kullanılmıştır. Çalışma diğer çalışmalardaki sonuçlarla kıyaslanarak sonuçlandırılmıştır.

🔖

Cite This Article

KÜPELİ, A. S., ENGİN, O., ENGİN, B. E. (2017). USING PARALLEL KANGAROO ALGORITHM TO SOLVE FLOW SHOP SCHEDULING PROBLEMS WITH FUZZY LEARNING EFFECT. Kapadokya Akademik Bakış, Cilt: 1 - Sayı: 1, 31–48. https://ajindex.com/articles/783635

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. Abdullah, S. ve Abdolrazzagh-Nezhad, M., 2014, Fuzzy job-shop scheduling problems: A review, Information Sciences, 278, 380-407.
  2. Agarwal, A., Colak, S. ve Eryarsoy, E., 2006, Improvement heuristic for the flow-shop scheduling problem: An adaptive-learning approach, European Journal of Operational Research, 169 (3), 801-815.
  3. Ahmadizar, F. ve Hosseini, L., 2012, Minimizing makespan in a single-machine scheduling problem with a learning effect and fuzzy processing times, The International Journal of Advanced Manufacturing Technology, 65 (1-4), 581-587.
  4. Ambika, G. ve Uthra, G., 2014, Branch and Bound Technique in Flow Shop Scheduling Using Fuzzy Processing Times, Annals of Pure and Applied Mathematics, Vol. 8 ( No. 2), 37-42
  5. Amirian, H. ve Sahraeian, R., 2015, Augmented ε-constraint method in multi-objective flowshop problem with past sequence set-up times and a modified learning effect, International Journal of Production Research, 53 (19), 5962-5976.
  6. Asadi, H., 2017, Apply Fuzzy Learning Effect with Fuzzy Processing Times for Single Machine Scheduling Problems, Journal of Manufacturing Systems, 42, 244-261.
  7. Bachman, A. ve Janiak, A., 2004, Scheduling jobs with position-dependent processing times, Journal of the Operational Research Society, 55, 257-263.
  8. Balin, S., 2011, Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation, Information Sciences, 181 (17), 3551-3569.
  9. Baysal, M. E., Durmaz, T., Sarucan, A. ve Engin, O., 2012, Açık atölye tipi çizelgeleme problemlerinin paralel kanguru algoritması ile çözümü, Journal of the Faculty of Engineering and Architecture of Gazi University, 27, 855-864.
  10. Behnamian, J. ve Fatemi Ghomi, S. M. T., 2014, Multi-objective fuzzy multiprocessor flowshop scheduling, Applied Soft Computing, 21, 139-148.
  11. Biskup, D., 1999, Single-machine scheduling with learning considerations, European Journal of Operational Research, 115, 173–178.
  12. Biskup, D., 2008, A state-of-the-art review on scheduling with learning effects, European Journal of Operational Research, 188 (2), 315-329.
  13. Cheng, T., Wu, C. ve Lee, W., 2008, Some scheduling problems with sum-of-processing-times-based and job-position-based learning effects, Information Sciences, 178 (11), 2476-2487.
  14. De Jong, J. R., 1957, THE EFFECTS OF INCREASING SKILL ON CYCLE TIME AND ITS CONSEQUENCES FOR TIME STANDARDS, Ergonomics, 1 (1), 51-60.
  15. Dong, Y., 2003, One Machine Fuzzy Scheduling to Minimize Total Weighted Tardiness, Earliness, and Recour