OPTIMASI MULTI TRAVELLING SALESMAN PROBLEM (M-TSP) UNTUK DISTRIBUSI PRODUK PADA HOME INDUSTRI TEKSTIL DENGAN ALGORITMA GENETIKA

Agung Mustika Rizki, Wayan Firdaus Mahmudy, Gusti Eka Yuliastuti

Abstract


In the field of textile industry, the distribution process is an important factor that can affect the cost of production. For that we need optimization on the distribution process to be more efficient. This problem is a model in the Multi Travelling Salesman Problem (M-TSP). Much research has been done to complete the M-TSP model. Among several methods that have been applied by other researchers, genetic algorithms are a workable method for solving this model problem. In this article the authors chose the genetic algorithm is expected to produce an optimal value with an efficient time. Based on the results of testing and analysis, obtained the optimal population amount of 120. For the optimal generation amount is 800. The test results related to the number of population and the number of generations are used as input to test the combination of CR and MR, obtained the optimal combination of CR = 0 , 4 and MR = 0.6 with a fitness value of 2.9964.

Keywords: Textile Industry, Multi Travelling Salesman Problem (M-TSP), Genetic Algorithm

Pada bidang industri tekstil, proses distribusi merupakan satu faktor penting yang dapat berpengaruh terhadap biaya produksi. Untuk itu diperlukan optimasi pada proses distribusi agar menjadi lebih efisien. Masalah seperti ini merupakam model dalam Multi Travelling Salesman Problem (M-TSP). Banyak penelitian telah dilakukan untuk menyelesaikan model M-TSP. Diantara beberapa metode yang telah diterapkan oleh peneiti lain, algoritma genetika adalah metode yang bisa diterapkan untuk penyelesaian permasalahan model ini. Dalam artikel ini penulis memilih algoritma genetika diharapkan dapat menghasilkan nilai yang optimal dengan waktu yang efisien. Berdasarkan hasil pengujian dan analisis, didapatkan jumlah populasi yang optimal sebesar 120. Untuk jumlah generasi yang optimal adalah sebesar 800. Hasil pengujian terkait jumlah populasi dan jumlah generasi tersebut dijadikan masukan untuk melakukan pengujian kombinasi  CR dan MR, didapatkan kombinasi yang optimal yakni CR=0,4 dan MR=0,6 dengan nilai fitness sebesar 2,9964.

Kata kunci: Industri Tekstil, Distribusi, Multi Travelling Salesman Problem (M-TSP), Algoritma Genetika


Full Text:

PDF

References


Sudaryanto, Ragimun, and R. R. Wijayanti, “Strategi Pemberdayaan UMKM Menghadapi Pasar Bebas Asean,” Www.Kemenkeu.Go.Id, pp. 1–32, 2014.

W. Lesmawati, A. Rahmi, and W. F. Mahmudy, “Optimization of Frozen Food Distribution using Genetic Algorithms,” J. Environ. Eng. Sustain. Technol., vol. 3, no. 1, pp. 51–58, 2016.

C. Wild, “The concept of distribution,” Stat. Educ. Res. J., vol. 5, no. 2, pp. 10–26, 2006.

M. Al Khuluqi, W. F. Mahmudy and A. Rahmi, “Profit Optimization Based on Total Production in Textile Home,” vol. 2, no. 2, pp. 109–117, 2016.

S. Yuan, B. Skinner, S. Huang, and D. Liu, “A new crossover approach for solving the multiple travelling salesmen problem using genetic algorithms,” Eur. J. Oper. Res., vol. 228, no. 1, pp. 72–82, 2013.

B. Soylu, “A general variable neighborhood search heuristic for multiple traveling salesmen problem,” Comput. Ind. Eng., vol. 90, pp. 390–401, 2015.

M. M. Paydar, I. Mahdavi, I. Sharafuddin, and M. Solimanpur, “Applying simulated annealing for designing cellular manufacturing systems using MDmTSP,” Comput. Ind. Eng., vol. 59, no. 4, pp. 929–936, 2010.

W. F. Mahmudy, “Optimasi Multi Travelling Salesman Problem ( M - TSP ) menggunakan algoritma genetika,” vol. 1, no. February, pp. 1–6, 2016.

E. Mayasari, “Penyelesaian Multiple-Travelling Salesman Problem Dengan Simulated Annealing,” 2012.

V. N. Wijayaningrum and W. F. Mahmudy, “Optimization of Ship’s Route Scheduling Using Genetic Algorithm,” Indones. J. Electr. Eng. Comput. Sci., vol. 2, no. 1, pp. 180–186, 2016.

F. Y. Saptaningtyas, “Multi Traveling Salesman Problem (MTSP) Dengan Algoritma Genetika Untuk Menentukan Rute Loper Koran Di Agen Surat Kabar,” vol. 7, no. 2, pp. 55–64, 2012.

P. Kotler, “Marketing Management,” Prentice Hall, New Jersey, pp. 1–10, 2000.

W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, “Modeling and Optimization of Part Type Selection and Loading Problem in Flexible Manufacturing System Using Real Coded Genetic Algorithms,” World Acad. Sci. Eng. Technol. Int. J. Mech. Aerospace, Ind. Mechatron. Manuf. Eng., vol. 7, no. 4, pp. 675–684, 2013.

Z. A. Afrouzy, S. H. Nasseri, and I. Mahdavi, “A genetic algorithm for supply chain configuration with new product development,” Comput. Ind. Eng., vol. 101, pp. 440–454, 2016.

S. Ene, Küçükolu, A. Aksoy, and N. Öztürk, “A genetic algorithm for minimizing energy consumption in warehouses,” vol. 114, pp. 973–980, 2016.




DOI: http://dx.doi.org/10.20527/klik.v4i2.86

Copyright (c) 2017 KLIK - KUMPULAN JURNAL ILMU KOMPUTER

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed by:

  
 

 

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.joomla
counter View My Stats