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 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 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 Penanganan Fuzzy Time Window pada Travelling Salesman Problem (TSP) dengan Penerapan Algoritma Genetika

MATICS ◽  
2017 ◽  
Vol 9 (1) ◽  
pp. 38
Author(s):  
Gusti Eka Yuliastuti ◽  
Wayan Firdaus Mahmudy ◽  
Agung Mustika Rizki
Keyword(s):  
Time Window ◽  
Travelling Salesman Problem ◽  
Test Results ◽  
Travel Agents ◽  
Level Of Satisfaction ◽  
Fitness Value ◽  
Fuzzy Time Window ◽  
Optimum Solution ◽  
The Traveling Salesman Problem ◽  
Selection Of

<p class="Text"><strong><span lang="EN-US">The route of the travel tour packages offered by travel agents is not considered optimum, so the level of satisfaction the tourist is not maximal. Selection of the route of the travel packages included in the traveling salesman problem (TSP). The problem that occurs is uncertain tourists visiting destinations at the best destinations timing hereinafter be referred to as the fuzzy time window problem. Therefore, the authors apply the genetic algorithm to solve the problem. Based on test results obtained optimum solution with the fitness value of 1.3291, a population size of 100, the number of generations of 1000, a combination of CR=0,4 and MR=0.6.</span></strong></p>

scholarly journals Tourist Route Optimization in the Context of Covid-19 Pandemic

2021 ◽  
Vol 13 (10) ◽  
pp. 5492
Author(s):  
Cristina Maria Păcurar ◽  
Ruxandra-Gabriela Albu ◽  
Victor Dan Păcurar
Keyword(s):  
Travelling Salesman Problem ◽  
Route Planning ◽  
Route Optimization ◽  
Travelling Salesman ◽  
Social Distancing ◽  
Tourist Destinations ◽  
Innovative Method ◽  
The Social ◽  
Immediate Response

The paper presents an innovative method for tourist route planning inside a destination. The necessity of reorganizing the tourist routes within a destination comes as an immediate response to the Covid-19 crisis. The implementation of the method inside tourist destinations can bring an important advantage in transforming a destination into a safer one in times of Covid-19 and post-Covid-19. The existing trend of shortening the tourist stay length has been accelerated while the epidemic became a pandemic. Moreover, the wariness for future pandemics has brought into spotlight the issue of overcrowded attractions inside a destination at certain moments. The method presented in this paper proposes a backtracking algorithm, more precisely an adaptation of the travelling salesman problem. The method presented is aimed to facilitate the navigation inside a destination and to revive certain less-visited sightseeing spots inside a destination while facilitating conformation with the social distancing measures imposed for Covid-19 control.

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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