Optimasi Biaya Distribusi Rantai Pasok Tiga Tingkat dengan Menggunakan Algoritma Genetika Adaptif dan Terdistribusi
Zulfahmi Indra(1*), Subanar Subanar(2)
(1) 
(2) 
(*) Corresponding Author
Abstract
Abstrak
Manajemen rantai pasok merupakan hal yang penting. Inti utama dari manajemen rantai pasok adalah proses distribusi. Salah satu permasalahan distribusi adalah strategi keputusan dalam menentukan pengalokasian banyaknya produk yang harus dipindahkan mulai dari tingkat manufaktur hingga ke tingkat pelanggan. Penelitian ini melakukan optimasi rantai pasok tiga tingkat mulai dari manufaktur-distributor-gosir-retail. Adapun pendekatan yang dilakukan adalah algoritma genetika adaptif dan terdistribusi. Solusi berupa alokasi banyaknya produk yang dikirim pada setiap tingkat akan dimodelkan sebagai sebuah kromosom. Parameter genetika seperti jumlah kromosom dalam populasi, probabilitas crossover dan probabilitas mutasi akan secara adaptif berubah sesuai dengan kondisi populasi pada generasi tersebut. Dalam penelitian ini digunakan 3 sub populasi yang bisa melakukan pertukaran individu setiap saat sesuai dengan probabilitas migrasi. Adapun hasil penelitian yang dilakukan 30 kali untuk setiap perpaduan nilai parameter genetika menunjukkan bahwa nilai biaya terendah yang didapatkan adalah 80,910, yang terjadi pada probabilitas crossover 0.4, probabilitas mutasi 0.1, probabilitas migrasi 0.1 dan migration rate 0.1. Hasil yang diperoleh lebih baik daripada metode stepping stone yang mendapatkan biaya sebesar 89,825.
Kata kunci— manajemen rantai pasok, rantai pasok tiga tingkat, algortima genetika adaptif, algoritma genetika terdistribusi.
Abstract
Supply chain management is critical in business area. The main core of supply chain management is the process of distribution. One issue is the distribution of decision strategies in determining the allocation of the number of products that must be moved from the level of the manufacture to the customer level. This study take optimization of three levels distribution from manufacture-distributor-wholeshale-retailer. The approach taken is adaptive and distributed genetic algorithm. Solution in the form of allocation of the number of products delivered at each level will be modeled as a chromosome. Genetic parameters such as the number of chromosomes in the population, crossover probability and adaptive mutation probability will change adaptively according to conditions on the population of that generation. This study used 3 sub-populations that exchange individuals at any time in accordance with the probability of migration. The results of research conducted 30 times for each value of the parameter genetic fusion showed that the lowest cost value obtained is 80,910, which occurs at the crossover probability 0.4, mutation probability 0.1, the probability of migration 0.1 and migration rate 0.1. This result has shown that adaptive and distributed genetic algorithm is better than stepping stone method that obtained 89,825.
Keywords— management supply chain, three level supply chain, adaptive genetic algorithm, distributed genetic algorithm.
Keywords
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[1] Siagian, Y.M., 2005, Supply Chain Management Dalam Dunia Bisnis, Penerbit PT Gramedia Widiasarana Indonesia, Jakarta.
[2] Indrajit, R. E., dan Djokopranoto, R., 2002, Konsep Manajemen Supply Chain, PT Gramedia Widiasarana Indonesia, Jakarta.
[3] Sunil, C., dan Peter, M.,2001,Supply Chain Management: Strategy, Planning, and Operation,Prentice-Hall, New Jersey:
[4] Zhu, Z., Yunlong, Z., dan Xiaoming, Z., 2008, A Integrated Contract Strategy in a Three-Echelon Supply Chain with Capacity Limitation under the Forecast
[5] Hua, J., dan Junhu, R., 2008, An Optimization Model of Multi-echelon Stocahastic Inventory System in the Supply Chain, International Conference on Risk Management and Engineering Management, pp:20-25.
[6] Yu, J. C., Lin, Y. S., Wang, K. J., dan Wee, H. M., 2009, Using AI Approach to Solve an Integrated Three-Echelon Supply Chain Model with Stra tegic Alliances,First Asian Conference on Intelligent Information and Database Systems, pp.259-264.
[7] Gupta, A., Narayan, V., Raj, A., Harsh, dan Nagaraju, D., 2012, A Comparative Study of Three Echelon Inventory Optimization using Genetic Algorithm and Particle Swarm optimization,International Journal of Trade Economics and Finance, 3(3), pp. 205-208.
[9] Király, A., Varga, T., Abonyi, J. 2012. Constrained Particle Swarm Optimization of Supply Chains, World Academy of Science, Engineering and Technology 67.
[8] Che, Z. H., Chiang, T. A., dan Kuo, Y. C., 2012, Multi-Echelon Reverse Supply Chain Network Design With Specified Returns Using Particle Swarm Optimization,International Journal of Innovative Computing Information and Control, 8(10), pp. 6719-6731.
[10] Yoshizumi, T. Okano, H. 2007, A Simulation-Based Algorithm for Supply Chain Optimization, Proceedings of the 2007 Winter Simulation Conference. Pg. 1924-1931
[11] Mastrocinque, E., Yuce, B., Lambiase, A., Packianather, M. S., 2013. A Multi-Objective Optimization for Supply Chain Network Using the Bees Algorithm, International Journal of Engineering Business Management. 5(38). Pg 1-11
[12] Chan, C., Lee, H.W.J., 2005. Successful Strategies in Supply Chain Management. Idea Group Publishing : USA
[13] Bäck, Th., Eiben, A.E., van der Vaart, N.A.L. 2000. An Empirical Study on Gas Without Parameters.Proceeding of the 6th International Conference on Parallel Problem Solving from Nature.
[14] Loraschi, A., Tettamanzi, A., Tomassini, M., Verda, P. 1995. Distributed Genetic Algorithms with an Application to Portfolio Selection Problems. Artificial Neural Nets and Genetic Algorithms. Springer-Verlag. pp 384-387.[15] Mejía-Olvera, M., Cantú-Paz, E., 1994. DGENESIS-Software for the Execution of Distributed Genetic Algorithms. Proceedings of the XX Conferencia Latinoamericana de Informática, pp. 935-946, Monterrey, México.Update,International Conference on Information Management, Innovation Management and Industrial Engineering, pp :62-65.
[16] Mahmudy, W.F., Rahman, M. A. 2011, Optimasi Fungsi Multi-Obyektif Berkendala Menggunakan Algoritma Genetika Adaptif dengan Pengkodean Real, Jurnal Ilmiah Kursor. Jawa Timur
DOI: https://doi.org/10.22146/ijccs.6546
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