Algoritme Particle Swarm Optimization (PSO) untuk Optimasi Perencanaan Produksi Agregat Multi-Site pada Industri Tekstil Rumahan
Keywords:Agregate Production Planning, Particle Swarm Optimization, Multi-site Industry
In the industrial world, companies need to manage their production areas well. One way is to implement aggregate production planning. The goal is that the production costs incurred by the company can be controlled properly. However, production planning cannot be formulated quickly. The problem is more complicated if the company has several production locations. The difference in location also affects the production references and standards applied in each location. Based on these problems, the authors propose to apply the Particle Swarm Optimization (PSO) algorithm to solve the problem of aggregate production planning in order to obtain the optimal solution for each production location. As a result, the algorithm proposed by the author can produce optimal and efficient solutions for 6 production sites. This is evidenced by the relatively short time required compared to the previous planning by the company.
O. Saracoglu, M. C. Arslan, and M. Turkay, “Aggregate Planning Problem from Sustainability Perspective,”Int. Conf. Adv. Logist. Transp. Aggreg., pp. 181–186, 2015.
L. Nafisah, Sutrisno, and Y. E. H. Hutagaol, “Perencanaan Produksi Menggunakan Goal Programming (Studi Kasus di Bakpia Pathuk 75 Yogyakarta),” Spektrum Ind., vol. 14, pp. 109–230, 2016.
I. A. Octavianti, N. W. Setyanto, C. Farela, and M. Tantrika, “Perencanaan Produksi Agregat ProdukTembakau Rajang P01 Dan P02 Di PT X,” J. Rekayasa dan Manaj. Sist. Ind., vol. 1, no. 2, pp. 264–274, 2012.
W. F. Mahmudy, “Improved Particle Swarm Optimization untuk Menyelesaikan Permasalahan Part TypeSelection dan Machine Loading pada Flexible Manufacturing System (FMS),” Konf. Nas. Sist. Inf. , Univ.Klabat, Airmadidi, Minahasa Utara, Sulawesi Utara, no. August, pp. 1003–1008, 2015.
W. F. Mahmudy, “Optimization of Part Type Selection and Machine Loading Problems in FlexibleManufacturing System Using Variable Neighborhood Search,” IAENG Int. J.Comput. Sci., vol. 42:3, no.July, pp. 254–264, 2015.6.I. Cholissodin and E. Riyandani, SWARM INTELLIGENCE (Teori & Case Study). Malang: Fakultas IlmuKomputer, Universitas Brawijaya, 2016
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