scholarly journals Mobile Application Searching of the Shortest Route on Delivery Order of CV. Alfa Fresh With Brute Force Algorithm

2019 ◽  
Vol 19 (3) ◽  
pp. 120
Author(s):  
Indri Ariyanti ◽  
M. Aris Ganiardi ◽  
Ulsa Oktari

Traveling Salesman Problem is a problem solving used in finding the shortest route to visit all nodes at once and then return to the initial node. Troubleshooting of the Traveling Salesman Problem using the Brute Force algorithm. The object of this research is the courier at CV. Alfa Fresh. The Brute Force algorithm provides a solution for Traveling Salesman Problems to select and determine the shortest routes to deliver orders from the office to the destination. The Brute Force algorithm is an algorithm that is used to match patterns with all routes to be traversed to find the shortest route pattern. The Brute Force algorithm works by enumerating all possible candidates. With this application can facilitate the courier in determining the closest route from the position of the courier.

2020 ◽  
Vol 29 (3) ◽  
pp. 572-579
Author(s):  
Vladimir Shinkarenko ◽  
Sergii Nezdoyminov ◽  
Svetlana Galasyuk ◽  
Larysa Shynkarenko

The article is devoted to the problem of constructing the optimal transport route of a bus excursion tour of a tour operator according to the minimal length criterion. Transportation expenses are an important part of a bus tour cost, and their minimization is a required condition for route development and planning. To solve this problem, the authors used the tour route calculating method as a kind of transport task, namely the task of the salesman. To solve this problem, one of the varieties of the transport problem, namely the salesman traveling problem, is applied. The essence of the traveling salesman problem is to find the shortest route between cities, if the distances between them are known. The beginning of the route and its end coincide, that is, the route is cyclic. The most popular in Ukraine sightseeing tours of tour operators to the Transcarpathian region are taken for the optimization. A mathematical model of the traveling salesman problem is made to construct an optimal transport route. The solution was found using the Microsoft Excel’s Solver add-in application program package. To solve the problem by this method, it was reduced to a special form and additional variables were introduced. The analysis helped the tour operator to check the existing sightseeing bus routes by the minimal length criterion. The results allowed making assumptions about the need to change some popular routes of Ukrainian tour operators in order to reduce transport costs. The method of bus tours evaluation of tour operators according to the minimal length criterion allows to check the tourist transportation optimality while planning the route and developing their own tourist product. The introduction of modern digital technologies and software to optimize the territorial organization of tourist routes is proposed, which will help tour operators of Ukraine in the design of bus tourist trips to nature and recreational locations in tourist regions. The development of an optimal model for the transportation of tourists on the highways and the reduction of their costs for consumption of transport services will contribute to the development of tourist trips in Ukraine. The necessity of using methods of geolocation of tourist resources for the construction of routes for visiting natural and cultural-historical monuments and tourist centers has been determined. The application of the proposed method will allow tour operators to reduce transport costs and, as a consequence, the total cost of the tourist product.


2021 ◽  
Vol 2 (1) ◽  
pp. 43-48
Author(s):  
Aswandi ◽  
Sugiarto Cokrowibowo ◽  
Arnita Irianti

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.


2019 ◽  
Vol 5 (2) ◽  
pp. 100-111
Author(s):  
Bib Paruhum Silalahi ◽  
Nurul Fathiah ◽  
Prapto Tri Supriyo

Ant Colony Optimization is one of the meta-heuristic methods used to solve combinatorial optimization problems that are quite difficult. Ant Colony Optimization algorithm is inspired by ant behavior in the real world to build the shortest path between food sources and their nests. Traveling Salesman Problem is a problem in optimization. Traveling Salesman Problem is a problem to find the minimum distance from the initial node to the whole node with each node must be visited exactly once and must return to the initial node. Traveling Salesman Problem is a non-deterministic polynomial-time complete problem. This research discusses the solution of the Traveling Salesman Problem using the Ant Colony Optimization algorithm and also using the exact algorithm. The results showed that the greater the size of the Traveling Salesman Problem case, the longer the execution time required. The results also showed that the execution times of the Ant Colony Optimization are much faster than the execution time of the exact method.


2020 ◽  
pp. 93-116
Author(s):  
Chris Bleakley

Chapter 6 examines one of the greatest unsolved challenges in mathematics - the problem of finding the best solution from a large number of possibilities. The Traveling Salesman Problem requires that the shortest tour of a group of cities is determined. Surprisingly, the only way to guarantee finding the shortest tour is to measure the length of all possible tours. Exhaustive search such as this is very slow. For centuries, mathematicians have sought to find fast algorithms for solving combinatorial search problems. The most famous was invented by Edsger Dijkstra in 1956. Dijkstra’s algorithm finds the shortest route between cities on a roadmap and is now used in all satellite navigation apps. The Gale-Shapley algorithm solves the problem of matching pairs of items according to user preferences. John Holland took the radical step of accelerating combinatorial search by mimicking natural evolution in a computer.


Author(s):  
Juwairiah Juwairiah ◽  
Dicky Pratama ◽  
Heru Cahya Rustamaji ◽  
Herry Sofyan ◽  
Dessyanto Boedi Prasetyo

The concept of Traveling Salesman Problem (TSP) used in the discussion of this paper is the Traveling Salesman Problem with Time Windows (TSP-TW), where the time variable considered is the time of availability of attractions for tourists to visit. The algorithm used for optimizing the solution of Traveling Salesman Problem with Time Windows (TSP-TW) is a genetic algorithm. The search for a solution for determining the best route begins with the formation of an initial population that contains a collection of individuals. Each individual has a combination of different tourist sequence. Then it is processed by genetic operators, namely crossover with Partially Mapped Crossover (PMX) method, mutation using reciprocal exchange method, and selection using ranked-based fitness method. The research method used is GRAPPLE. Based on tests conducted, the optimal generation size results obtained in solving the TSP-TW problem on the tourist route in the Province of DIY using genetic algorithms is 700, population size is 40, and the combination of crossover rate and mutation rate is 0.70 and 0.30 There is a tolerance time of 5 seconds between the process of requesting distance and travel time and the process of forming a tourist route for the genetic algorithm process.


2007 ◽  
Vol 5 (1) ◽  
pp. 1-9
Author(s):  
Paulo Henrique Siqueira ◽  
Sérgio Scheer ◽  
Maria Teresinha Arns Steiner

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Jin Zhang ◽  
Li Hong ◽  
Qing Liu

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.


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