Journals / Endüstri Mühendisliği / 2026 / Cilt: 37 Sayı: 2

BLOKLANMANIN DİKKATE ALINDIĞI STOKASTİK DAĞITIK ESNEK AKIŞ TİPİ ÇİZELGELEME PROBLEMİ

Feriştah Özçelik(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ)0000-0003-0329-203X
Tuğba Saraç(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ)0000-0002-8115-3206
Pages
273–292
Publication Date
2026-08-28
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AI Summary & Key Takeaways

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

Bu çalışma, dağıtık sistemlerde bloklanma etkisini incelemesi ve işlem sürelerini stokastik ele alması yönleriyle öne çıkmaktadır.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Bu çalışmada, dağıtık esnek akış tipi çizelgeleme problemi bloklanma kısıtı altında ele alınmıştır.
📊
Key Findings & Data:Tüm test problemlerinde stokastik çözümün değeri (VSS) pozitif olarak elde edilmiştir.
💡
Conclusion & Impact:Bu çalışma, dağıtık sistemlerde bloklanma etkisini incelemesi ve işlem sürelerini stokastik ele alması yönleriyle öne çıkmaktadır.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Dağıtık esnek akış tipi çizelgeleme problemi · bloklanma kısıtı · stokastik işlem süreleri

Özet

Bu çalışmada, dağıtık esnek akış tipi çizelgeleme problemi bloklanma kısıtı altında ele alınmıştır. Bloklanma, ara stoğa izin verilmeyen çok aşamalı üretim sistemlerinde bir sonraki aşamadaki tüm makineler dolu olduğunda işin tamamlanmasına rağmen makineyi terk edememesi nedeniyle makinenin bloke olmasıdır. Ele alınan problemde işlem süreleri stokastiktir. Problemin çözümü için iki aşamalı stokastik programlama modeli önerilmiştir. Önerilen çözüm yaklaşımının performansı rassal türetilen test problemleri kullanılarak gösterilmiştir. Tüm test problemlerinde stokastik çözümün değeri (VSS) pozitif olarak elde edilmiştir. Bu sonuç problemi stokastik ele almanın katkısını göstermiştir. Bu çalışma, dağıtık sistemlerde bloklanma etkisini incelemesi ve işlem sürelerini stokastik ele alması yönleriyle öne çıkmaktadır.

🔖

Cite This Article

Özçelik, F., Saraç, T. (2026). BLOKLANMANIN DİKKATE ALINDIĞI STOKASTİK DAĞITIK ESNEK AKIŞ TİPİ ÇİZELGELEME PROBLEMİ. Endüstri Mühendisliği, Cilt: 37 Sayı: 2, 273–292. https://doi.org/10.46465/endustrimuhendisligi.1849162

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References (37)

  1. Cai, J. ve Wang, L. (2024). A shuffled frog leaping algorithm with q-learning for distributed hybrid flow shop scheduling problem with energy-saving. Journal of Artificial Intelligence and Soft Computing Research, 14. Doi: https://doi.org/10.2478/jaiscr-2024-0006
  2. Cui, H., Li, X., Gao, L. ve Zhang, C. (2024). Multi-population genetic algorithm with greedy job insertion inter-factory neighbourhoods for multi-objective distributed hybrid flow-shop scheduling with unrelated-parallel machines considering tardiness. International Journal of Production Research, 62(12), 4427-4445. Doi: https://doi.org/10.1080/00207543.2023.2262616
  3. Cui, H., Li, X., Gao, L., Xiang, Z., Zhou, W., Yu, Y. ve Fu, L. (2025). Cooperation-based bi-level rescheduling method for multi-objective distributed hybrid flow shop with unrelated parallel machines under multi-type disturbances. International Journal of Computer Integrated Manufacturing, 1-23. Doi: https://doi.org/10.1080/0951192X.2025.2478005
  4. Deng, L., Di, Y. ve Wang, L. (2024). A reinforcement-learning-based 3-D estimation of distribution algorithm for fuzzy distributed hybrid flow-shop scheduling considering on-time-delivery. IEEE transactions on cybernetics, 54(2), 1024-1036. Doi: https://doi.org/10.1109/TCYB.2023.3336656
  5. Deng, L., Qiu, Y., Gong, W., Di, Y. ve Li, C. (2024). A dynamic decision-driven memetic algorithm for fuzzy distributed hybrid flow shop rescheduling considering quality control. Expert Systems with Applications, 257, 125002. Doi: https://doi.org/10.1016/j.eswa.2024.125002
  6. Di, Y., Deng, L. ve Zhang, L. (2024). A collaborative-learning multi-agent reinforcement learning method for distributed hybrid flow shop scheduling problem. Swarm and Evolutionary Computation, 91, 101764. Doi: https://doi.org/10.1016/j.swevo.2024.101764
  7. Geng, Y. D. ve Li, J. Q. (2023a). A knowledge-driven multiobjective algorithm for distributed hybrid flowshop with group and carryover setup in glass manufacturing systems. Computers & Industrial Engineering, 181, 109325. Doi: https://doi.org/10.1016/j.cie.2023.109325
  8. Geng, Y. ve Li, J. (2023b). An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems. CMES-Computer Modeling in Engineering & Sciences, 134(1), 241-266. Doi: https://doi.org/10.32604/cmes.2022.020307
  9. Gholami, H. ve Sun, H. (2023). Toward automated algorithm configuration for distributed hybrid flow shop scheduling with multiprocessor tasks. Knowledge-Based Systems, 264, 110309. Doi: https://doi.org/10.1016/j.knosys.2023.110309
  10. Ji, Q., Han, Y., Wang, Y., Gong, D. ve Gao, K. (2025). A Q-learning-driven genetic algorithm for the distributed hybrid flow shop group scheduling problem with delivery time Windows. Information Sciences, 121971. Doi: https://doi.org/10.1016/j.ins.2025.121971
  11. Jia, Y., Yan, Q. ve Wang, H. (2023). Q-learning driven multi-population memetic algorithm for distributed three-stage assembly hybrid flow shop scheduling with flexible preventive maintenance. Expert Systems with Applications, 232, 120837. Doi: https://doi.org/10.1016/j.eswa.2023.120837
  12. Lei, D. ve Su, B. (2023). A multi-class teaching–learning-based optimization for multi-objective distributed hybrid flow shop scheduling. Knowledge-Based Systems, 263, 110252. Doi: https://doi.org/10.1016/j.knosys.2023.110252
  13. Li, J. Q., Chen, X. L., Duan, P. Y. ve Mou, J. H. (2022). KMOEA: A knowledge-based multiobjective algorithm for distributed hybrid flow shop in a prefabricated system. IEEE Transactions on Industrial Informatics, 18(8), 5318-5329. Doi: https://doi.org/10.1109/TII.2021.3128405
  14. Liu, C., Cheng, Q., Cheng, C., Chu, H., Liu, Z. ve Zhang, W. (2025). Enhancing energy efficiency in distributed hybrid forging flow-shops: a novel scheduling approach to capitalise on residual forging heat. International Journal of Production Research, 63(6), 2170-2189. Doi: https://doi.org/10.1080/00207543.2024.2398580
  15. Liu, F., Li, G., Lu, C., Yin, L. ve Zhou, J. (2024). A tri-individual iterated greedy algorithm for the distributed hybrid flow shop with blocking. Expert Systems with Applications, 237, 121667. Doi: https://doi.org/10.1016/j.eswa.2023.121667
  16. Liu, H., Zhao, F., Wang, L., Xu, T. ve Dong, C. (2025). Evolutionary multitasking memetic algorithm for distributed hybrid flow-shop scheduling problem with deterioration effect. IEEE Transactions on Automation Science and Engineering, 22, 1390-1404. Doi: https://doi.org/10.1109/TASE.2024.3365518
  17. Liu, J., Han, Y., Wang, Y., Liu, Y. ve Zhang, B. (2024). Distributed hybrid flowshop scheduling with consistent sublots under delivery time windows: A penalty lot-assisted iterated greedy algorithm. Egyptian Informatics Journal, 28, 100566. Doi: https://doi.org/10.1016/j.eij.2024.100566
  18. Lu, C., Zhou, J., Gao, L., Li, X. ve Wang, J. (2024). Modeling and multi-objective optimization for energy-aware scheduling of distributed hybrid flow-shop. Applied Soft Computing, 156, 111508. Doi: https://doi.org/10.1016/j.asoc.2024.111508
  19. Luo, L. ve Yan, X. (2025). Scheduling of stochastic distributed hybrid flow-shop by hybrid estimation of distribution algorithm and proximal policy optimization. Expert Systems with Applications, 126523. Doi: https://doi.org/10.1016/j.eswa.2025.126523
  20. Luo, L. ve Yan, X. (2025). Selection hyperheuristic with knowledge-based Q-learning for dynamic distributed hybrid flow shop scheduling problem considering operation inspection. Swarm and Evolutionary Computation, 95, 101936. Doi: https://doi.org/10.1016/j.swevo.2025.101936
  21. Qin, H., Han, Y., Chen, Q., Wang, L., Wang, Y., Li, J. ve Liu, Y. (2023). Energy-efficient iterative greedy algorithm for the distributed hybrid flow shop scheduling with blocking constraints. IEEE Transactions on Emerging Topics in Computational Intelligence, 7(5), 1442-1457. Doi: https://doi.org/10.1109/TETCI.2023.3271331
  22. Saraç, T. ve Özçelik, F. (2025). Stokastik teslim zamanlı tek makine çizelgeleme problemi için bir harmoni arama algoritması. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 27(80), 247-56. Doi: https://doi.org/10.21205/deufmd.2025278011
  23. Shao, W., Shao, Z. ve Pi, D. (2022). A multi-neighborhood-based multi-objective memetic algorithm for the energy-efficient distributed flexible flow shop scheduling problem. Neural Computing and Applications, 34(24), 22303-22330. Doi: https://doi.org/10.1007/s00521-022-07714-3
  24. Shao, W., Shao, Z. ve Pi, D. (2024). Lot sizing and scheduling problem in distributed heterogeneous hybrid flow shop and learning-driven iterated local search algorithm. IEEE Transactions on Automation Science and Engineering, 21(4), 6483-6497. Doi: https://doi.org/10.1109/TASE.2023.3326301
  25. Song, H., Li, J., Du, Z., Yu, X., Xu, Y., Zheng, Z. ve Li, J. (2025). A Q-learning driven multi-objective evolutionary algorithm for worker fatigue dual-resource-constrained distributed hybrid flow shop. Computers & Operations Research, 175, 106919. Doi: https://doi.org/10.1016/j.cor.2024.106919
  26. Sun, X., Shen, W., Fan, J., Vogel-Heuser, B., Bi, F. ve Zhang, C. (2025). Deep Reinforcement Learning-based Multi-Objective Scheduling for Distributed Heterogeneous Hybrid Flow Shops with Blocking Constraints. Engineering, 46, 278-291. Doi: https://doi.org/10.1016/j.eng.2024.11.033
  27. Wang, J. J. ve Wang, L. (2022). A cooperative memetic algorithm with learning-based agent for energy-aware distributed hybrid flow-shop scheduling. IEEE Transactions on Evolutionary Computation, 26(3), 461-475. Doi: https://doi.org/10.1109/TEVC.2021.3106168
  28. Wang, Z., Deng, Q., Zhang, L., Li, H. ve Li, F. (2023). Joint optimization of integrated mixed maintenance and distributed two-stage hybrid flow-shop production for multi-site maintenance requirements. Expert Systems with Applications, 215, 119422. Doi: https://doi.org/10.1016/j.eswa.2022.119422
  29. Xu, T., Zhao, F., Zhang, J., Tang, J. ve Zhou, H. (2025). A feature based neural network model for distributed flexible flow shop scheduling considering worker and transportation factors. Computers & Industrial Engineering, 110917. Doi: https://doi.org/10.1016/j.cie.2025.110917
  30. Yu, F., Lu, C., Zhou, J. ve Yin, L. (2024). Mathematical model and knowledge-based iterated greedy algorithm for distributed assembly hybrid flow shop scheduling problem with dual-resource constraints. Expert Systems with Applications, 239, 122434. Doi: https://doi.org/10.1016/j.eswa.2023.122434
  31. Zhang, J. ve Cai, J. (2023). A dual-population genetic algorithm with Q-learning for multi-objective distributed hybrid flow shop scheduling problem. Symmetry, 15(4), 836. Doi: https://doi.org/10.3390/sym15040836
  32. Zhang, W., Li, C., Gen, M., Yang, W. ve Zhang, G. (2024). A multiobjective memetic algorithm with particle swarm optimization and Q-learning-based local search for energy-efficient distributed heterogeneous hybrid flow-shop scheduling problem. Expert Systems with Applications, 237, 121570. Doi: https://doi.org/10.1016/j.eswa.2023.121570
  33. Zheng, Y., Peng, N., Qi, H., Gong, G., Huang, D., Zhu, K., Liu, J. ve Liu, G. (2025). An improved memetic algorithm for distributed hybrid flow shop scheduling problem with operation inspection and reprocessing. Measurement and Control, 58(1), 68-96. Doi: https://doi.org/10.1177/00202940241245241
  34. Zhu, Q., Gao, K., Huang, W., Ma, Z. ve Slowik, A. (2024). Q-Learning-Assisted Meta-Heuristics for Scheduling Distributed Hybrid Flow Shop Problems. Computers, Materials & Continua, 80(3). Doi: https://doi.org/10.32604/cmc.2024.055244
  35. Zuo, Y., Zhao, F. ve Yu, Y. (2025). Two-stage learning scatter search algorithm for the distributed hybrid flow shop scheduling problem with machine breakdown. Expert Systems with Applications, 259, 125344. Doi: https://doi.org/10.1016/j.eswa.2024.125344
  36. Zuo, Y., Zhao, F. ve Zhang, J. (2024). A bi-population cooperative scatter search algorithm for distributed hybrid flow shop scheduling with machine breakdown. Computers & Industrial Engineering, 197, 110624. Doi: https://doi.org/10.1016/j.cie.2024.110624
  37. Zuo, Y., Zhao, F. ve Zhang, J. (2025). A knowledge-driven scatter search algorithm for the distributed hybrid flow shop scheduling problem. Engineering Applications of Artificial Intelligence, 142, 109915. Doi: https://doi.org/10.1016/j.engappai.2024.109915