Multiple Travelling Salesman Proble

Multiple Travelling Salesman Problem (MTSP) merupakan salah satu masalah optimasi yang
dapat dijadikan acuan dalam menentukan rute terpendek dimana membutuhkan lebih dari satu salesman untuk mengunjungi sejumlah kota dan kembali ke kota awal. Tujuan penyelesaian MTSP adalah mencari lintasan Hamilton dari setiap rute yang dilalui oleh salesman yang mempunyai bobot total paling kecil, sehingga menghasilkan jarak tempuh terpendek dengan biaya yang minimum. Beberapa algoritma yang telah dilakukan sebagai alternatif penyelesaian masalah MTSP diantaranya Algoritma Genetika dengan metode 2-OPT (GA2-OPT) dan Algoritma Koloni Semut (ACO). Dalam GA2-OPT, MTSP diselesaikan dengan Algoritma Genetika (GA) untuk setiap iterasinya kemudian metode 2-OPT digunakan untuk meningkatkan solusi dari iterasi yang dilakukan menggunakan GA. Sedangkan pada Algoritma ACO, penyelesaian MTSP didasarkan pada perilaku koloni semut dalam mencari makanan dengan menemukan jarak terpendek yang diperoleh dari jejak feromon. Dalam penulisan artikel ini, penulis menerapkan dua algoritma tersebut untuk menentukan algoritma yang efisien dalam menyelesaikan masalah MTSP.

Results (English) 1:

Multiple Traveling Salesman Problem (MTSP) is one of the optimization problemreferable to determine shortest routes which require more than one salesman to visit a number of cities and a return to town early. The purpose of the completion of the MTSP is looking for the path of Hamilton from any route that traveled by salesmen who have a total weighting, thus generating the shortest mileage with minimum cost. Several algorithms have been conducted as an alternative problem solving including Genetic Algorithm with MTSP method 2-OPT (GA2-OPT) and Ant Colony algorithm (ACO). In the GA2-OPT, MTSP solved by Genetic Algorithms (GA) for each iterasinya 2-OPT methods are then used to enhance the solution from the iteration is done using GA. whereas in the MTSP, the completion of the ACO Algorithm based on the behavior of ant colonies in search of food by finding the shortest distance of the trail pheromone. In the writing of this article, the authors apply the two algorithms to determine an efficient algorithm in solving the problem of MTSP.

Results (English) 2:

Multiple Travelling Salesman Problem (MTSP) is one of the optimization problem that
can be used as a reference in determining the shortest route which require more than one salesman to visit a number of cities and back into town early. The purpose is to seek settlement MTSP Hamilton trajectory of each route followed by a salesman who has the smallest total weighting, resulting in the shortest travel distance with minimum cost. Several algorithms have been done as an alternative to solving problems such MTSP Genetic Algorithm with 2-OPT method (ga2-OPT) and Ant Colony Algorithm (ACO). In ga2-OPT, MTSP solved by Genetic Algorithm (GA) for each iteration then 2-OPT method is used to improve the solution of iterations were performed using GA. While the ACO algorithm, MTSP settlement based on the behavior of ant colonies in search of food by finding the shortest distance obtained from pheromone trail. In writing this article, the authors apply these two algorithms to determine an efficient algorithm to solve the problem MTSP.