OPTIMISASI DISTRIBUSI SPARE PART KENDARAAN DENGAN MENGGUNAKAN PARTICLE SWARM OPTIMIZATION (PSO) DAN GENETIC ALGORITHM (GA)

SUMARDIYANTO, HERY (2023) OPTIMISASI DISTRIBUSI SPARE PART KENDARAAN DENGAN MENGGUNAKAN PARTICLE SWARM OPTIMIZATION (PSO) DAN GENETIC ALGORITHM (GA). S2 thesis, Universitas Mercu Buana - Menteng.

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Abstract

Latar Belakang: Transportasi dalam sistem rantai pasok pada industri manufaktur sepeda motor memiliki fungsi yang sangat penting karena berperan dalam melakukan distribusi barang, baik barang berupa bahan baku, komponen, barang setengah jadi maupun barang jadi (Safaei & Jardine, 2018). Nilai ekonomis transportasi dalam menjalankan peran tersebut adalah melakukan perpindahan persediaan dari lokasi asal ke lokasi tujuan tertentu dalam sistem manajemen rantai pasok. Kinerja transportasi akan menentukan kinerja pengadaan, produksi dan manajemen hubungan pelanggan. Metode: Penelitian ini menggunakan pendekatan Vehicle Routing Problem (VRP) melalui metode optimasi Particle Swarm Optimization (PSO) dan Genetic Algorithm (GA). Analisis data menggunakan software Matlab 2020. Hasil: Penelitian menunjukkan bahwa proses distribusi spare part dari vendor ke pabrik terjadi peningkatan efisiensi angkutan armada yang meliputi frekuensi distribusi armada dari 40 unit/hari menjadi 8 unit per hari, sedangkan kebutuhan armada dari 40 unit per hari menjadi 4 unit per hari dengan optimasi GA, sedangkan 5 unit per hari dengan optimasi PSO. Dari sisi Biaya transportasi terjadi penurunan yang awalnya Rp 2,769,330 menjadi Rp 2,679,113 dengan optimasi PSO dan Rp 2,000,058 dengan optimasi GA. Kesimpulan: Penelitian ini bermanfaat untuk menghilangkan potensi pemborosan dalam hal penggunaan armada dan penumpukan armada di pabrik sehingga akan berdampak pada pengurangan biaya transportasi dari armada masing-masing pemasok. Kata kunci: Efisiensi transportasi armada, Vehicle Routing Problem, Particle Swarm Optimization, Genetic Algorithm Background: Transportation in the supply chain system in the motorcycle manufacturing industry has a very important function because it plays a role in distributing goods, both goods in the form of raw materials, components, semi-finished goods and finished goods (Safaei & Jardine, 2018). The economic value of transportation in carrying out this role is to move inventory from the location of origin to a certain destination location in the supply chain management system. Transportation performance will determine the performance of procurement, production and customer relationship management. Methods: This research uses the Vehicle Routing Problem (VRP) approach through Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) optimization methods. Data analysis using Matlab 2020 software. Results: Research shows that the process of distributing spare parts from vendors to factories has increased the efficiency of fleet transportation which includes the frequency of fleet distribution from 40 units/day to 8 units per day, while fleet needs from 40 units per day to 4 units per day with GA optimization, while 5 units per day with PSO optimization. In terms of transportation costs, there was a decrease from IDR 2,769,330 to IDR 2,679,113 with PSO optimization and IDR 2,000,058 with GA optimization. Conclusion: This research is useful for eliminating the potential for waste in terms of fleet usage and fleet buildup at factories so that it will have an impact on reducing transportation costs from each supplier's fleet. Keywords: Fleet transportation efficiency, Vehicle Routing Problem, Particle Swarm Optimization, Genetic Algorithm

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