HİBRİT AKIŞ TİPİ ÇİZELGELEME PROBLEMİNİN PARÇACIK SÜRÜ OPTİMİZASYON ALGORİTMASIYLA ÇÖZÜMÜNE BAŞLANGIÇ ÇÖZÜMLERİN ETKİSİ

Öz

Bu çalışmada, birden fazla aşama, her aşamada özdeş paralel makineler ve makinelerde işlenen işler arası geçişlerde sıra bağımlı hazırlık süresini içeren hibrit akış tipi çizelgeleme problemi sunulmuştur. Metasezgisel yöntemler bu karmaşık problemlerin çözümünde tercih kullanılmaktadır. Metasezgisel yöntemlerde optimum çözüm ararken, oluşturulan başlangıç çözümlerin nihai sonuca etkisi oldukça önemlidir. Hibrit akış tipi çizelgeleme probleminin çözümüne önerilen parçacık sürü optimizasyon (PSO) algoritmasına farklı başlangıç çözüm oluşturma yöntemlerinin etkisi incelenmiştir. Dört farklı başlangıç çözüm oluşturma yaklaşımı sonuçları karşılaştırılmıştır. Nawaz, Enscore, Ham (NEH) sezgiselinin diğer yöntemlerden daha rekabetçi olduğu sonucuna ulaşılmıştır.

Anahtar Kelimeler

• Hibrit akış tipi çizelgeleme
• metasezgisel
• PSO

Kaynakça

1. Abdel-Basset, M., Manogaran, G., El-Shahat, D., & Mirjalili, S. (2018). A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem. Future Generation Computer Systems, 85, 129-145.
2. Alaykýran, K., Engin, O., & Döyen, A. (2007). Using ant colony optimization to solve hybrid flow shop scheduling problems. The international journal of advanced manufacturing technology, 35(5-6), 541-550.
3. Bargaoui, H., Driss, O. B., & Ghédira, K. (2017). A novel chemical reaction optimization for the distributed permutation flowshop scheduling problem with makespan criterion. Computers & Industrial Engineering, 111, 239-250.
4. Ben-Daya, M., & Al-Fawzan, M. (1998). A tabu search approach for the flow shop scheduling problem. European journal of operational research, 109(1), 88-95.
5. Campbell HG, Dudek RA, Smıth BL. (1970). A heuristic algorithm for the n job, m machine sequencing problem”. Management Science, 16(10), 630-637.
6. Chen, R. M., Wu, C. L., Wang, C. M., & Lo, S. T. (2010). Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB. Expert systems with applications, 37(3), 1899-1910.
7. Chen, R. M. (2011). Particle swarm optimization with justification and designed mechanisms for resource-constrained project scheduling problem. Expert Systems with Applications, 38(6), 7102-7111.
8. Engin, O., & Döyen, A. (2004). A new approach to solve hybrid flow shop scheduling problems by artificial immune system. Future generation computer systems, 20(6), 1083-1095.

9. Fernandez-Viagas, V., & Framinan, J. M. (2015). NEH-based heuristics for the permutation flowshop scheduling problem to minimise total tardiness. Computers & Operations Research, 60, 27-36.
10. Fernandez-Viagas, V., Molina-Pariente, J. M., & Framinan, J. M. (2018). New efficient constructive heuristics for the hybrid flowshop to minimise makespan: A computational evaluation of heuristics. Expert Systems with Applications, 114, 345-356.
11. Gupta, J. N. (1988). Two-stage, hybrid flowshop scheduling problem. Journal of the operational Research Society, 39(4), 359-364.
12. Ho JC, Chang Y. (1991). A new heuristic for the n-Job, m-Machine flow-shop problem. European Journal of Operational Research, 52(2), 194-202.
13. Huang, S., Tian, N., Wang, Y., & Ji, Z. (2016). Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization. SpringerPlus, 5(1), 1432.
14. Janiak, A., Kozan, E., Lichtenstein, M., & Oğuz, C. (2007). Metaheuristic approaches to the hybrid flow shop scheduling problem with a cost-related criterion. International Journal of Production Economics, 105(2), 407-424.
15. Jia, Q., & Seo, Y. (2013). An improved particle swarm optimization for the resource-constrained project scheduling problem. The International Journal of Advanced Manufacturing Technology, 67(9-12), 2627-2638.
16. Jin, Z., Yang, Z., & Ito, T. (2006). Metaheuristic algorithms for the multistage hybrid flowshop scheduling problem. International Journal of Production Economics, 100(2), 322-334.
17. Johnson, S. M. (1954). Optimal two‐and three‐stage production schedules with setup times included. Naval research logistics quarterly, 1(1), 61-68.
18. Komaki, G. M., Teymourian, E., & Kayvanfar, V. (2016). Minimising makespan in the two-stage assembly hybrid flow shop scheduling problem using artificial immune systems. International Journal of Production Research, 54(4), 963-983.
19. Koulinas, G., Kotsikas, L., & Anagnostopoulos, K. (2014). A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem. Information Sciences, 277, 680-693.
20. Li, J. Q., Pan, Q. K., & Wang, F. T. (2014). A hybrid variable neighborhood search for solving the hybrid flow shop scheduling problem. Applied Soft Computing, 24, 63-77.
21. Liao, C. J., Tjandradjaja, E., & Chung, T. P. (2012). An approach using particle swarm optimization and bottleneck heuristic to solve hybrid flow shop scheduling problem. Applied Soft Computing, 12(6), 1755-1764.
22. Lin, T. L., Horng, S. J., Kao, T. W., Chen, Y. H., Run, R. S., Chen, R. J., ... & Kuo, I. H. (2010). An efficient job-shop scheduling algorithm based on particle swarm optimization. Expert Systems with Applications, 37(3), 2629-2636.
23. Lin, J. T., & Chen, C. M. (2015). Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing. Simulation Modelling Practice and Theory, 51, 100-114.
24. Lu, C., Gao, L., Pan, Q., Li, X., & Zheng, J. (2019). A multi-objective cellular grey wolf optimizer for hybrid flowshop scheduling problem considering noise pollution. Applied Soft Computing, 75, 728-749.
25. Marichelvam, M. K., Geetha, M., & Tosun, Ö. (2020). An improved particle swarm optimization algorithm to solve hybrid flowshop scheduling problems with the effect of human factors–A case study. Computers & Operations Research, 114, 104812.
26. Moslehi, G., & Mahnam, M. (2011). A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. International Journal of Production Economics, 129(1), 14-22.
27. Murata, T., Ishibuchi, H., & Tanaka, H. (1996). Multi-objective genetic algorithm and its applications to flowshop scheduling. Computers & Industrial Engineering, 30(4), 957-968.
28. Naderi, B., & Ruiz, R. (2014). A scatter search algorithm for the distributed permutation flowshop scheduling problem. European Journal of Operational Research, 239(2), 323-334.
29. Nawaz M, Enscore EJ, Ham I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, the International Journal Of Management Science, 11(1), 91-95.
30. Nouiri, M., Bekrar, A., Jemai, A., Trentesaux, D., Ammari, A. C., & Niar, S. (2017). Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns. Computers & Industrial Engineering, 112, 595-606.
31. Palmer D. (1965). Sequencing jobs through a multi-stage process in the minimum total time-a quick method of obtaining a near optimum. Operational Research Quarterly, 16(1), 101-107.
32. Pan, Q. K., Tasgetiren, M. F., & Liang, Y. C. (2008). A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem. Computers & Operations Research, 35(9), 2807-2839.
33. Pan, Q. K., Tasgetiren, M. F., Suganthan, P. N., & Chua, T. J. (2011). A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information sciences, 181(12), 2455-2468.
34. Pan, Q. K., Wang, L., Li, J. Q., & Duan, J. H. (2014). A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation. Omega, 45, 42-56.
35. Ribas, I., Leisten, R., & Framiñan, J. M. (2010). Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective. Computers & Operations Research, 37(8), 1439-1454.
36. Ribas, I., Companys, R., & Tort-Martorell, X. (2011). An iterated greedy algorithm for the flowshop scheduling problem with blocking. Omega, 39(3), 293-301.
37. Ruiz, R., & Vázquez-Rodríguez, J. A. (2010). The hybrid flow shop scheduling problem. European journal of operational research, 205(1), 1-18.
38. Ruiz, R., & Vázquez-Rodríguez, J. A. (2010). The hybrid flow shop scheduling problem. European journal of operational research, 205(1), 1-18.
39. Sha, D. Y., & Hsu, C. Y. (2006). A hybrid particle swarm optimization for job shop scheduling problem. Computers & Industrial Engineering, 51(4), 791-808.
40. Sha, D. Y., & Hsu, C. Y. (2008). A new particle swarm optimization for the open shop scheduling problem. Computers & Operations Research, 35(10), 3243-3261.
41. Suresh, K., & Kumarappan, N. (2013). Hybrid improved binary particle swarm optimization approach for generation maintenance scheduling problem. Swarm and Evolutionary Computation, 9, 69-89.
42. Tang, L., Liu, W., & Liu, J. (2005). A neural network model and algorithm for the hybrid flow shop scheduling problem in a dynamic environment. Journal of Intelligent Manufacturing, 16(3), 361-370.
43. Torabi, S. A., Sahebjamnia, N., Mansouri, S. A., & Bajestani, M. A. (2013). A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem. Applied Soft Computing, 13(12), 4750-4762.
44. Widmer M, Hertz A. (1989). A new heuristic method for the flowshop sequencing problem. European Journal of Operational Research, 41, 186-193, 1989.
45. Yagmahan, B., & Yenisey, M. M. (2008). Ant colony optimization for multi-objective flow shop scheduling problem. Computers & Industrial Engineering, 54(3), 411-420.
46. Zarrouk, R., Bennour, I. E., & Jemai, A. (2019). A two-level particle swarm optimization algorithm for the flexible job shop scheduling problem. Swarm Intelligence, 13(2), 145-168.
47. Zhang, C., Sun, J., Zhu, X., & Yang, Q. (2008). An improved particle swarm optimization algorithm for flowshop scheduling problem. Information Processing Letters, 108(4), 204-209.
48. Zhang, G., Shao, X., Li, P., & Gao, L. (2009). An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers & Industrial Engineering, 56(4), 1309-1318.