scholarly journals OPTIMASI MULTI TRAVELLING SALESMAN PROBLEM (M-TSP) UNTUK DISTRIBUSI PRODUK PADA HOME INDUSTRI TEKSTIL DENGAN ALGORITMA GENETIKA

2017 ◽  
Vol 4 (2) ◽  
pp. 125
Author(s):  
Agung Mustika Rizki ◽  
Wayan Firdaus Mahmudy ◽  
Gusti Eka Yuliastuti
Keyword(s):  
Genetic Algorithm ◽  
Textile Industry ◽  
Travelling Salesman Problem ◽  
Test Results ◽  
Travelling Salesman ◽  
Fitness Value ◽  
Optimal Value ◽  
Distribution Process ◽  
Optimal Population ◽  
The Cost

<p><em>In the field of textile industry, the distribution process is an important factor that can affect the cost of production. For that we need optimization on the distribution process to be more efficient. This problem is a model in the Multi Trave</em><em>l</em><em>ling Salesman Problem (M-TSP). Much research has been done to complete the M-TSP model. Among several methods that have been applied by other researchers, genetic algorithms are a workable method for solving this model problem. In this article the authors chose the genetic algorithm is expected to produce an optimal value with an efficient time. Based on the results of testing and analysis, obtained the optimal population amount of 120. For the optimal generation amount is 800. The test results related to the number of population and the number of generations are used as input to test the combination of CR and MR, obtained the optimal combination of CR = 0 , 4 and MR = 0.6 with a fitness value of 2.9964.</em></p><p><em><strong>Keywords</strong></em><em>: Textile Industry, Multi Travelling Salesman Problem (M-TSP), Genetic Algorithm</em></p><p><em>Pada bidang industri tekstil, proses distribusi merupakan satu faktor penting yang dapat berpengaruh terhadap biaya produksi. Untuk itu diperlukan optimasi pada proses distribusi agar menjadi lebih efisien. Masalah seperti ini merupakam model dalam Multi Travelling Salesman Problem (M-TSP). Banyak penelitian telah dilakukan untuk menyelesaikan model M-TSP. Diantara beberapa metode yang telah diterapkan oleh peneiti lain, algoritma genetika adalah metode yang bisa diterapkan untuk penyelesaian permasalahan model ini. Dalam artikel ini penulis memilih algoritma genetika diharapkan dapat menghasilkan nilai yang optimal dengan waktu yang efisien. Berdasarkan hasil pengujian dan analisis, didapatkan jumlah populasi yang optimal sebesar 120. Untuk jumlah generasi yang optimal adalah sebesar 800. Hasil pengujian terkait jumlah populasi dan jumlah generasi tersebut dijadikan masukan untuk melakukan pengujian kombinasi  CR dan MR, didapatkan kombinasi yang optimal yakni CR=0,4 dan MR=0,6 dengan nilai fitness sebesar 2,9964.</em></p><p><em><strong>Kata kunci</strong></em><em>: </em><em>Industri Tekstil, Distribusi, Multi Travelling Salesman Problem (M-TSP), Algoritma Genetika</em></p>

scholarly journals Implementation of Genetic Algorithm to Solving Travelling Salesman Problem with Time Window (TSP-TW) for Scheduling Tourist Destinations in Malang City

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Gusti Eka Yuliastuti ◽  
Wayan Firdaus Mahmudy ◽  
Agung Mustika Rizki
Keyword(s):  
Genetic Algorithm ◽  
Time Window ◽  
Travelling Salesman Problem ◽  
Optimal Scheduling ◽  
Test Results ◽  
Travelling Salesman ◽  
Tourist Destinations ◽  
Fitness Value ◽  
Time Window Constraints ◽  
Test Result

In doing travel to some destinantions, tourist certainly want to be able to visit many destinations with the optimal scheduling so that necessary in finding the best route and not wasting lots of time travel. Several studies have addressed the problem but does not consider other factor which is very important that is the operating hours of each destination or hereinafter referred as the time window. Genetic algorithm proved able to resolve this travelling salesman problem with time window constraints. Based on test results obtained solutions with the fitness value of 0,9856 at the time of generation of 800 and the other test result obtained solution with the fitness value of 0,9621 at the time of the combination CR=0,7 MR=0,3.

scholarly journals Optimasi Beban Mengajar Dosen Pendidikan Informatika di STKIP Bumi Persada Menggunakan Algoritme Genetika

2019 ◽  
Vol 1 ◽  
pp. 972
Author(s):  
Teuku Afriliansyah
Keyword(s):  
Genetic Algorithm ◽  
Fixed Number ◽  
Routine Activity ◽  
Test Results ◽  
Optimal Parameters ◽  
Study Program ◽  
Random Mutation ◽  
Fitness Value ◽  
Selection Test ◽  
The Cost

The cost of teaching lecturers is a routine activity conducted by all universities, especially the maintainers of departments in each faculty. This is done because the number of courses planned students are in every semester is always different and faced with a relatively fixed number of lecturers. Determining the teaching burden of lecturers must be done so that the teaching burden of lecturers does not exceed the maximum possible limit and the teaching process is done in accordance with the interest of lecturer study. Study Program of informatics Education High School and Educational Sciences Earth Persada Lhokseumawe still do the process of determining the teaching burden of the lecturer with the manual so that it takes a little time because it must adjust the infirmity Courses with a lecturer study interest. One of the methods of optimization that is able to solve the problem is genetic algorithm. The genetic algorithm process in this research includes representation with integer numbers, crossover methods with one cut point crossover, mutation methods with Reciprocalexchange mutation and random mutation, as well as selection methods with elitism Selection. Test results that have been tested show optimal parameters i.e. population size 60, combination of CR and Mr Value respectively 0.4, Sertta generation of 3576 with the largest fitness value produced is 0.082846.

scholarly journals Representasi Matriks untuk Proses Crossover Pada Algoritma Genetika untuk Optimasi Travelling Salesman Problem

Matematika ◽  
2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Ismi Fadhillah ◽  
Yurika Permanasari ◽  
Erwin Harahap
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Matrix Representation ◽  
Travelling Salesman Problem ◽  
Optimal Number ◽  
Travelling Salesman ◽  
Combinatorial Optimization Problems ◽  
Fitness Value ◽  
The City

Abstrak. Travelling Salesman Problem (TSP) merupakan salah satu permasalahan optimasi kombinatorial yang biasa terjadi dalam kehidupan sehari-hari. Permasalahan TSP yaitu mengenai seseorang yang harus mengunjungi semua kota tepat satu kali dan kembali ke kota awal dengan jarak tempuh minimal. TSP dapat diselesaikan dengan menggunakan metode Algoritma Genetika. Dalam Algoritma Genetika, representasi matriks merupakan representasi kromosom yang menunjukan sebuah perjalanan. Jika dalam perjalanan tersebut melewati n kota maka akan dibentuk matriks n x n. Matriks elemen Mij dengan baris i dan kolom j dimana entry M(i,j) akan bernilai 1 jika dan hanya jika kota i dikunjungi sebelum kota j dalam satu perjalanan tersebut, selain itu M(i,j)=0. Crossover adalah mekanisme yang dimiliki algoritma genetika dengan menggabungkan dua kromosom sehingga menghasilkan anak kromosom yang mewarisi ciri-ciri dasar dari parent. Algoritma Genetika selain melibatkan populasi awal dalam proses optimasi juga membangkitkan populasi baru melalui proses crossover, sehingga dapat memberikan daftar variabel yang optimal bukan hanya solusi tunggal. Dari hasil proses crossover dalam contoh kasus TSP melewati 6 kota, terdapat 2 kromosom anak terbaik dengan nilai finess yang sama yaitu 0.014. Algoritma Genetika dapat berhenti pada generasi II karena berturut-turut mendapat nilai fitness tertinggi yang tidak berubahKata kunci : Travelling Salesman Program (TSP), Algoritma Genetika, Representasi Matriks, Proses Crossover Abstract. Travelling Salesman Problem (TSP) is one of combinatorial optimization problems in everyday life. TSP is about someone who had to visit all the cities exactly once and return to the initial city with minimal distances. TSP can be solved using Genetic Algorithms. In a Genetic Algorithm, a matrix representation represents chromosomes which indicates a journey. If in the course of the past n number of city will set up a matrix n x n. The matrix element Mij with row i and column j where entry M (i, j) will be equal to 1 if and only if the city i before the city j visited in one trip. In addition to the M (i, j) = 0. Crossover is a mechanism that is owned by the Genetic Algorithm to combine the two chromosomes to produce offspring inherited basic characteristics of the parent. Genetic Algorithms in addition to involve the population early in the optimization process will also generate new populations through the crossover process, so as to provide optimal number of variables is not just a single solution. From the results of the crossover process in the case of TSP passing through six cities, there are two the best offspring with the same finess value which is 0.014. Genetic Algorithms can be stopped on the second generation due to successive received the highest fitness value unchanged.Keywords: Travelling Salesman Program (TSP), Genetic Algorithm, Matrix Representation, Crossover Process

scholarly journals OPTIMASI PENDISTRIBUSIAN BARANG FARMASI MENGGUNAKAN ALGORITMA GENETIKA

2018 ◽  
Vol 5 (2) ◽  
pp. 159
Author(s):  
Febri Ramadhani ◽  
Ficry Agam Fathurrachman ◽  
Restu Fitriawanti ◽  
Angki Christiawan Rongre ◽  
Vivi Nur Wijayaningrum
Keyword(s):  
Genetic Algorithm ◽  
Selection Process ◽  
Minimal Distance ◽  
Exchange Method ◽  
Cut Point ◽  
Reciprocal Exchange ◽  
Fitness Value ◽  
Distribution Process ◽  
Distance Data ◽  
The Cost

<p><em>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. </em><br /> <br /><em><strong>Keywords</strong>: Genetic Algorithm, Distribution, Pharmaceutical, Optimization </em><br /><br /><em>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. </em><br /> <br /><em><strong>Kata kunci</strong>: Algoritma Genetika, Distribusi, Farmasi, Optimasi</em></p>

scholarly journals Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019 ◽  
Vol 11 (9) ◽  
pp. 2571
Author(s):  
Xujing Zhang ◽  
Lichuan Wang ◽  
Yan Chen
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Optimal Solution ◽  
Material Distribution ◽  
Low Carbon ◽  
Nsga Ii ◽  
Management Objectives ◽  
Multi Objective Genetic Algorithm ◽  
Distribution Process ◽  
The Cost

Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

scholarly journals Experimental Study of a Hybrid Genetic Algorithm for the Multiple Travelling Salesman Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Maha Ata Al-Furhud ◽  
Zakir Hussain Ahmed
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Hybrid Genetic Algorithm ◽  
Travelling Salesman ◽  
Complex Optimization ◽  
Problem Instances ◽  
Search Approach ◽  
Multiple Travelling Salesman Problem

The multiple travelling salesman problem (MTSP), an extension of the well-known travelling salesman problem (TSP), is studied here. In MTSP, starting from a depot, multiple salesmen require to visit all cities so that each city is required to be visited only once by one salesman only. It is NP-hard and is more complex than the usual TSP. So, exact optimal solutions can be obtained for smaller sized problem instances only. For large-sized problem instances, it is essential to apply heuristic algorithms, and amongst them, genetic algorithm is identified to be successfully deal with such complex optimization problems. So, we propose a hybrid genetic algorithm (HGA) that uses sequential constructive crossover, a local search approach along with an immigration technique to find high-quality solution to the MTSP. Then our proposed HGA is compared against some state-of-the-art algorithms by solving some TSPLIB symmetric instances of several sizes with various number of salesmen. Our experimental investigation demonstrates that the HGA is one of the best algorithms.

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