Hidayat, Miftakhul and STP., M.Sc., Aunur Rofiq Mulyarto and S.T, M.T, Danang Triagus Setiyawan (2022) Optimasi Penentuan Lokasi Gudang Komoditas Agroindustri Dengan Metode Algoritma Genetika (Studi Kasus Distribusi Beras Miskin Di Perum Bulog Divisi Regional Bojonegoro). Sarjana thesis, Universitas Brawijaya.
Abstract
Kondisi saat ini Perum Bulog memiliki 3 gudang yang tersebar pada tiap wilayah. Wilayah tersebut yaitu Kabupaten Tuban, Kabupaten Bojonegoro dan Kabupaten Lamongan. Ketiga gudang tersebut melayani 1229 titik distribusi. Dalam proses distribusi terdapat permasalahan seperti banyaknya titik distribusi, minimnya gudang dan sumber daya hingga menyebabkan keterlambatan pengiriman. Permasalahan tersebut dapat diatasi dengan menambah gudang dan memindahkan gudang ke titik yang optimal. Adapun tujuan penelitian ini yaitu Mengidentifikasi lokasi gudang distribusi ditinjau dari sisi jarak untuk melayani titik distribusi beras miskin (Raskin) Perum Bulog saat ini dan menganalisis penentuan lokasi gudang yang optimal berdasarkan metode algoritma genetika. Penelitian ini metode untuk menyelesaikan permasalahan lokasi gudang menggunakan solusi algoritma genetika. Algoritma Genetika yaitu teknik metaheuristik algoritma yang memanfaatkan proses seleksi alamiah atau sering disebut proses evolusi. Prosedur evolusi pada Algoritma Genetika disimulasikan dengan meniru proses adaptasi dan seleksi alam secara biologis. Pada proses pencarian membutuhkan operasi genetik yang terdiri atas proses mutasi dan crossover serta seleksi individu melalui kromosom berdasarkan nilai fitness. Dalam penggunaan algoritma genetika menggunakan mekanisme stokastik yaitu populasi, elitisme, crossover, dan mutasi. Hasil dari algoritma genetika menghasilkan fitness akhir yaitu 258.437 dengan gudang terbuka yaitu gudang A,E,F,G dan H. Dari hasil alokasi gudang dengan algoritma genetika dibandingkan dengan alokasi perum bulog menghasilkan penurunan 30% dari sisi total jarak pada alokasi gudang Perum Bulog mendapatkan total jarak sebesar 31.911 km dan algoritma genetika mendapatkan total jarak sebesar 25.843 km. Alokasi gudang Perum Bulog dan algoritma genetika memiliki beberapa perbedaan. Perbedaan dari jumlah gudang yang dibangun algoritma genetika lebih banyak. Hasil alokasi gudang dengan algoritma genetika memiliki keunggulan dari sisi jarak dibandingkan dengan alokasi gudang Perum Bulog. Penurunan total jarak sebesar 21,35% dengan perbedaan 6.068 km, jarak terpendek 30,15% dengan perbedaan 0,38 km, jarak terpanjang 18,25% dengan perbedaan 5,98 km dan rata rata jarak sebesar 22,50% dengan perbedaan 5,003 km. Penelitian ini hanya menggunakan variabel jarak. Jika menambahkan variabel baru yaitu permintaan atau pagu akan menghasilkan alokasi gudang yang berbeda dengan hasil yang didapatkan pada penelitian ini. Selain itu dapat ditambahkan variabel biaya dan kapasitas dari gudang karena penambahan jumlah gudang dapat mempengaruhi biaya. Sedangkan semakin banyak gudang maka kapasitas gudang yang diperlukan semakin besar.
English Abstract
The current condition of Perum Bulog has 3 warehouses scattered in each region. These areas are Tuban Regency, Bojonegoro Regency and Lamongan Regency. The third warehouse serves 1229 distribution points. In the distribution process there are problems such as the number of distribution points, the lack of warehouses and resources causing delays in delivery. These problems can be overcome by adding warehouses and moving warehouses to optimal points. The purpose of this study is to steal the location of the distribution warehouse in terms of distance to serve the current Perum Bulog distribution point for poor rice (Raskin) and to analyze the optimal warehouse building based on the genetic algorithm method. This research method to solve the warehouse location problem uses a genetic algorithm solution. Genetic Algorithm is an algorithmic metaheuristic technique that utilizes the process of natural selection or often called the process of evolution. Procedures in the evolution of Genetic Algorithms are simulated by imitating the processes of biological adaptation and natural selection. The search process requires genetic operations consisting of mutation and crossover processes as well as individual selection through chromosomes based on fitness values. In the use of genetic algorithms using stochastic mechanisms, namely population, elitism, crossover, and mutation. The results of the genetic algorithm produce a final fitness of 258,437 with open warehouses, namely warehouses A, E, F, G and H. From the results of the allocation of warehouses with the genetic algorithm compared to the allocation of Perum Bulog, it results in a 30% reduction in terms of the total distance in the allocation of Warehouses to Perum Bulog. total distance of 31,911 km and genetics get a total distance of 25,843 km. The location of the Bulog Perum warehouse and the genetic algorithm have several differences. The difference from the number of warehouses built by the genetic algorithm is more. The results of the allocation of warehouses using the genetic algorithm have an advantage in terms of distance compared to the allocation of warehouses for Perum Bulog. The total distance reduction was 21.35% with a difference of 6,068 km, the shortest distance was 30.15% with a difference of 0.38 km, the longest distance was 18.25% with a difference of 5.98 km and the average mileage was 22.50% with a difference of 5.003 km. In this study, only using the distance variable. If you add a new variable, namely demand or ceiling, it will result in a warehouse allocation that is different from the results obtained in this study. In addition, variable costs and capacity of the warehouse can be added because the addition of the number of warehouses can affect costs. Meanwhile, the more warehouses, the greater the required warehouse capacity.
| Item Type: | Thesis (Sarjana) |
|---|---|
| Identification Number: | 0522100489 |
| Uncontrolled Keywords: | Algoritma Genetika, Facility Location Problem , Raskin, Facility Location Problem, Genetic Algorithm, Raskin |
| Subjects: | 300 Social sciences > 338 Production > 338.1 Agriculture > 338.16 Production efficiency |
| Divisions: | Fakultas Teknologi Pertanian > Teknologi Industri Pertanian |
| Depositing User: | soegeng sugeng |
| Date Deposited: | 19 May 2023 01:32 |
| Last Modified: | 19 May 2023 01:32 |
| URI: | http://repository.ub.ac.id/id/eprint/199854 |
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