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Optimasi Penjadwalan Flow Shop Menggunakan Algoritma Hybrid Differential Evolution

DOI: https://doi.org/10.21070/r.e.m.v1i2.404

Author (s)


(1) * Rudi Nurdiansyah   (Universitas Negeri Malang)  
        Indonesia
(*) Corresponding Author

Abstract


Penjadwalan produksi merupakan bagian integral di dalam sistem manufaktur. Artikel ini menyelesaikan permasalahan penjadwalan flow shop dengan fungsi obyektif total flow time. Dalam penjadwalan, total flow time menghasilkan konsumsi yang stabil terhadap sumber daya, perputaran job yang cepat serta meminimalkan work in process inventory. Permasalahan penjadwalan flow shop tergolong pada permasalahan optimasi kombinatorial yang merupakan NP-hard. Saat ini, penggunaan algoritma metaheuristik banyak digunakan untuk memecahkan kasus optimasi kombinatorial, termasuk penjadwalan flow shop. Salah satu yang memiliki performa yang baik adalah Algoritma Differential Evolution. Untuk meningkatkan kualitas solusinya, Algoritma Differential Evolution akan ditambahkan dengan prosedur local search yang dinamakan Hybrid Differential Evolution. Untuk mengetahui performa dari algoritma tersebut, dilakukan pengujian menggunakan data penjadwalan flow shop yang ada pada OR-Library. Performa Hybrid Differential Evolution akan dibandingkan dengan algoritma lain. Hasil pengujian menunjukkan bahwa Hybrid Differential Evolution memberikan performa yang lebih baik dibandingkan dengan algoritma lain.

Keywords

Penjadwalan flow shop, total flow time, hybrid differential evolution



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References


[1] Davendra D, Zelinka I, Bialic-Davendra M, Senkerik R, Jasek R (2013). Discrete self-organising migrating algorithm for flow-shop scheduling with no-wait makespan. Math Comput Model 57:100–110.

[2] Pinedo, M.L.: Scheduling: theory, algorithms, and systems. Springer (2012)

[3] Javadi B, Saidi-Mehrabad M, Haji A, Mahdavi I, Jolai F, MahvadiAmiri N (2008) No-wait flow shop scheduling using fuzzy multiobjective linear programming. J Franklin Inst 345:452–467

[4] Yagmahan, B. and Yenisey, M. M. (2010). A multi-objective ant colony system algorithm for flow shop scheduling problem. Expert Systems with Applications, Vol. 37 No. 2, pp. 1361-1368.

[5] Yi Zhang, Xiaoping Li, Qian Wang. (2009). Hybrid genetic algorithm for permutation flowshop scheduling problems with total flowtime minimization. European Journal of Operational Research Vol. 196. pp. 869–876.

[6] Gao, K. Z., Pan, Q. K., & Li, J. Q. (2011). Discrete harmony search algorithm for the no-wait flow shop scheduling problem with total flow time criterion. The International Journal of Advanced Manufacturing Technology, 56(5-8), 683-692.

[7] Framinan, J.M., Leisten, R., Ruiz-Usano, R., 2005. Comparison of heuristics for flowtime minimisation in permutation flowshops. Computers and Operations Research 32, 1237–1254.

[8] Azizoglu M, Cakmark E, Kondakci S (2001) A flexible flowshop problem with total flow time minimization. Eur J Oper Res 132:528–538.

[9] Salmasi, N., Logendran, R., & Skandari, M. R. (2010). Total flow time minimization in a flowshop sequence-dependent group scheduling problem. Computers & Operations Research, 37(1), 199-212

[10] Jarboui, B., Eddaly, M., & Siarry, P. (2009). An estimation of distribution algorithm for minimizing the total flowtime in permutation flowshop scheduling problems. Computers & Operations Research, 36(9), 2638-2646.

[11] Taillard, E. (1993) ‘Benchmarks for basic scheduling problems’, european journal of operational research Vol. 64 No.2 pp.278-285.

[12] Yagmahan, B. dan Yenisey, M. M. (2008), “Ant Colony Optimization for Multi-Objective Flow Shop Scheduling Problem”, Computers & Industrial Engineering, Vol. 54, No. 3, hal. 411-420.

[13] Ishibuchi, H., Misaki, S. dan Tanaka, H. (1995), “Modified Simulated Annealing Algorithms for The Flow Shop Sequencing Problem”, European Journal of Operational Research, Vol. 81, No. 2, hal. 388–398.

14] Riyanto, O.A.W., & Santosa, B. (2015). ACO-LS Algorithm for Solving No-wait Flow Shop Scheduling Problem. Intelligence in the Era of Big Data. Vol. 516 p. 89.

[15] Kordoghli, B., Jmali, M., Saadallah, S., dan Liouene, N. (2010), “Multi-Objective Scheduling of Flow Shop Problems in Finishing Factories using Genetic Algorithm”, Journal or Textile an Apparel, Technology and Management, Vol. 6, No. 3, hal. 1-10.

[16] Lian, Z., Gu, X. dan Jiao, B. (2006), “A Similar Particle Swarm Optimization Algorithm for Permutation Flow Shop Scheduling to Minimize Makespan”, Applied Mathematics and Computation, Vol. 175, hal. 773–785.

[17] Gao, H. dan Liu, X. (2007), “Improved Artificial Immune Algorithm and Its Applications on Permutation Flow Shop Sequencing Problems”, Information Technology Journal, Vol. 6, No. 6, hal. 929–933.

[18] Storn, R. dan Price, K. (1997), “Differential Evolution - A Simple and Efficient Heuristic for Global Optimization Over Continuous Space”, Journal of Global Optimization, Vol. 11, hal. 341-359.

[19] Qian, B., Wang, L., Hu, R., Wang, W. L., Huang, D. X., and Wang, X. (2008) ‘A Hybrid differential evolution method for permutation flow-shop scheduling’, The International Journal of Advanced Manufacturing Technology, Vol. 38 No.7-8 pp.757-777.

[20] Tasgetiren, M.F, Liang, Y.C, Sevkli, M, Gencyilmaz, G, (2004), “Differential Evolution for Permutation Flowshop Sequencing Problem with Makespan Criterion”, Dept. of Management, Fatih University, Istambul-Turkey.

[21] Pan, Q.K., Tasgetiren, M.F. dan Liang, Y.C. (2008), ”A Discrete Differential Evolution Algorithm for The Permutation Flowshop Scheduling Problem”, Computers & Industrial Engineering, Vol. 55, hal. 795–816.

[22] Mingyong, L. dan Erbao, C. (2010),” An Improved Differential Evolution Algorithm for Vehicle Routing Problem with Simultaneous Pickups and Deliveries and Time Windows”, Engineering Applications of Artificial Intelligence, Vol. 23, hal. 188–195.

[23] Noman, N. dan Iba H. (2008), “Accelerating Differential Evolution Using an Adaptive Local Search”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 1.

[24] Sauer, J.G. dan Coelho, L. (2008), “Discrete Differential Evolution with Local Search to Solve the Traveling Salesman Problem: Fundamentals and Case Studies”, IEEE International Conference on Cybernetic Intelligent Systems, London, hal. 1-6.

[25] Zamuda, A., Brest, J., Boskovic, B. dan Zumer, V. (2009), “Differential Evolution with Self-adaptation and Local Search for Constrained Multiobjective Optimization”, IEEE Congress on Evolutionary Computation, Trondheim, hal. 195-202.

[26] Santosa, B. dan Willy, P. (2011), Metoda Metaheuristik : Konsep dan Implementasi, Guna Widya, Surabaya.


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10.21070/r.e.m.v1i2.404




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