OPTIMASI PENDISTRIBUSIAN BARANG FARMASI MENGGUNAKAN ALGORITMA GENETIKA

Febri Ramadhani, Ficry Agam Fathurrachman, Restu Fitriawanti, Angki Christiawan Rongre, Vivi Nur Wijayaningrum

Abstract


Distribution is an activity of distributing goods from factory to agents. Distribution process is considered efficient if the process of distribution of goods done with a minimal distance, so that the time and cost required for the distribution process will also be smaller. Genetic algorithm is used to optimize the pharmaceutical goods distribution process by finding the order of agents that each vehicle must visit during the distribution process. The data used is the cost and distance data between factory and each agent. One-cut point method is used for crossover process, reciprocal exchange method is used for mutation process, and elitism method for selection process. Based on the test result that has been done, the optimal parameters which are used to produce the best solution, such as the population size is 45, the generation number is 70, and the combination of cr and mr is 0.8 and 0.3. By using the best parameters, the resulting fitness value is in the range 0.014909 up to 0.017642.

Keywords: Genetic Algorithm, Distribution, Pharmaceutical, Optimization

Distribusi merupakan kegiatan menyalurkan barang dari pabrik ke agen. Proses distribusi dianggap efisien jika proses penyaluran barang dilakukan dengan jarak yang minimal, sehingga waktu dan biaya yang dibutuhkan untuk proses distribusi juga akan semakin kecil. Algoritma genetika digunakan untuk melakukan optimasi pada proses distribusi barang farmasi dengan mencari solusi berupa urutan agen yang harus dikunjungi oleh setiap kendaraan saat proses distribusi. Data yang digunakan adalah data biaya dan jarak antara pabrik dengan masing-masing agen. Metode one-cut point digunakan untuk proses crossover, metode reciprocal exchange digunakan untuk proses mutasi, dan metode elitism untuk proses seleksi. Berdasarkan hasil pengujian yang telah dilakukan, parameter optimal yang digunakan untuk menghasilkan solusi terbaik, antara lain ukuran populasi sebanyak 45, generasi sebanyak 70, serta kombinasi cr dan mr yaitu 0.8 dan 0.3. Dengan menggunakan parameter terbaik tersebut, nilai fitness yang dihasilkan berada pada rentang 0.014909 sampai dengan 0.017642.

Kata kunci: Algoritma Genetika, Distribusi, Farmasi, Optimasi


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DOI: http://dx.doi.org/10.20527/klik.v5i2.151

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