A Two Stage Method for the Multiple Traveling Salesman Problem

2020 ◽  
Vol 11 (3) ◽  
pp. 79-91
Author(s):  
Azcarie Manuel Cabrera Cuevas ◽  
Jania Astrid Saucedo Martínez ◽  
José Antonio Marmolejo Saucedo
Keyword(s):  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Difficulty Level ◽  
Median Problem ◽  
Two Stage ◽  
Multiple Traveling Salesman Problem ◽  
Solution Methods ◽  
The Many ◽  
The Traveling Salesman Problem

The variation of the traveling salesman problem (TSP) with multiple salesmen (m-TSP) has been studied for many years resulting in diverse solution methods, both exact and heuristic. However, the high difficulty level on finding optimal (or acceptable) solutions has opposed the many efforts of doing so. The proposed method regards a two stage procedure which implies a modified version of the p-Median Problem (PMP) alongside the TSP, making a partition of the nodes into subsets that will be assigned to each salesman, solving it with Branch & Cut (B&C), in the first stage. This is followed by the routing, applying an Ant Colony Optimization (ACO) metaheuristic algorithm to solve a TSP for each subset of nodes. A case study was reviewed, detailing the positioning of five vehicles in strategic places in the Mexican Republic.

scholarly journals Solving Multiple Traveling Salesman Problem by Meerkat Swarm Optimization Algorithm

2019 ◽  
Vol 54 (3) ◽  
Author(s):  
Belal Al-Khateeb ◽  
Mohammed Yousif
Keyword(s):  
Traveling Salesman Problem ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Real Life ◽  
Traveling Salesman ◽  
Swarm Optimization ◽  
Total Cost ◽  
Life Problems ◽  
Multiple Traveling Salesman Problem ◽  
The Traveling Salesman Problem

Multiple Traveling Salesman Problem (MTSP) is one of various real-life applications, MTSP is the extension of the Traveling Salesman Problem (TSP). TSP focuses on searching of minimum or shortest path (traveling distance) to visit all cities by salesman, while the primary goal of MTSP is to find shortest path for m paths by n salesmen with minimized total cost. Wherever, total cost means the sum of distances of all salesmen. In this work, we proposed metaheuristic algorithm is called Meerkat Swarm Optimization (MSO) algorithm for solving MTSP and guarantee good quality solution in reasonable time for real-life problems. MSO is a metaheuristic optimization algorithm that is derived from the behavior of Meerkat in finding the shortest path. The implementation is done using many dataset from TSPLIB95. The results demonstrate that MSO in most results is better than another results that compared in average cost that means the MSO superior to other results of MTSP.

scholarly journals Model Penentuan Rute Terpendek Penjemputan Sampah Menggunakan Metode MTSP dan Algoritma Genetika

2021 ◽  
Vol 2 (1) ◽  
pp. 43-48
Author(s):  
Aswandi ◽  
Sugiarto Cokrowibowo ◽  
Arnita Irianti
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Individual Fitness ◽  
Problem Method ◽  
Multiple Traveling Salesman Problem ◽  
Shortest Route ◽  
Fitness Value ◽  
The Creation ◽  
The Traveling Salesman Problem

Garbage pick-ups performed by two or more people must have a route in their pickup. However, it is not easy to model the route of the pickup that each point must be passed and each point is only passed once. Now, the method to create a route has been done a lot, one of the most commonly used methods is the creation of routes using the Traveling Salesman Problem method. Traveling Salesman Problem is a method to determine the route of a series of cities where each city is only traversed once. In this study, the shortest route modeling was conducted using Multiple Traveling Salesman Problem and Genetic Algorithm to find out the shortest route model that can be passed in garbage pickup. In this study, datasets will be used as pick-up points to then be programmed to model the shortest routes that can be traveled. The application of Multiple Traveling Salesman Problem method using Genetic Algorithm shows success to model garbage pickup route based on existing dataset, by setting the parameters of 100 generations and 100 population and 4 salesmen obtained 90% of the best individual opportunities obtained with the best individual fitness value of 0.05209. The test was conducted using BlackBox testing and the results of this test that the functionality on the system is 100% appropriate.

scholarly journals OPTIMIZATION OF POSTAL ROUTES BY GENETIC ALGORITHM FOR SOLVING THE MULTIPLE TRAVELING SALESMAN PROBLEM

2015 ◽  
Vol 3 ◽  
pp. 043-049 ◽  
Author(s):  
Martin Macík ◽  
Jozef Štefunko
Keyword(s):  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Transport Costs ◽  
Heuristic Solution ◽  
Multiple Traveling Salesman Problem ◽  
Postal Services ◽  
Postal Network ◽  
High Level ◽  
The Traveling Salesman Problem ◽  
Postal Sector

High level of competition on postal market increases demands on reliability of postal services and lowering of transport costs. This can be achieved by optimizing the routing of postal vehicles. The article discusses the possibilities of such optimization by using graph theory. It describes basic methods of finding optimal routes using a graph. The approach, used in this article, assesses the possibility of applying meta-heuristic solution to the traveling salesman problem in the postal sector. Simulation of methods described has been applied on a regional postal network. Results showed that the software used proves to be sufficiently functional for the field of postal transport networks.

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