scholarly journals Perancangan Aplikasi Penjadwalan Traveling Salesman Problem dengan Algoritma Genetika

2012 ◽  
Vol 3 (2) ◽  
pp. 977
Author(s):  
Hendy Tannady ◽  
Andrew Verrayo Limas
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Industry ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Operational Cost ◽  
Travelling Salesman ◽  
Optimal Sequence ◽  
Chain Management ◽  
Efficiency And Effectiveness ◽  
A Company

Supply chain management plays an important role in enhancing the efficiency and effectiveness of manufacturing industry business process. In this research, the problem is taken from a sales division in a company in determining the optimal sequence when delivering goods into nine cities. This problem is oftenreferred as travelling salesman problem. This problem is considered important since the optimal sequence can cut off operational cost. Creating an artificial intelligence for the company in determining the location and the optimal sequence of delivering goods is the main objective of this research. A genetic algorithm is utilized to determine the location and the optimal sequence. While for processing the data and concluding the result, researcher designed a Java-based application that provides the capability of automatic computing. The result of this computation is a sequence of locations with a fitness number for each. The best fitness number for the sequence location will be used for the final result and the conclusion to answer the company’s problem.

The Algorithm Animation of Genetic Algorithm of Travelling Salesman Problem

2013 ◽  
Vol 411-414 ◽  
pp. 2013-2016 ◽  
Author(s):  
Guo Zhi Wen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Algorithm Animation ◽  
The Traveling Salesman Problem

The traveling salesman problem is analyzed with genetic algorithms. The best route map and tendency of optimal grade of 500 cities before the first mutation, best route map after 15 times of mutation and tendency of optimal grade of the final mutation are displayed with algorithm animation. The optimal grade is about 0.0455266 for the best route map before the first mutation, but is raised to about 0.058241 for the 15 times of mutation. It shows that through the improvements of algorithms and coding methods, the efficiency to solve the traveling problem can be raised with genetic algorithms.

scholarly journals Implementasi Algoritma Genetika dan Google Maps API Dalam Penyelesaian Traveling Salesman Problem with Time Window (TSP-TW) Pada Penjadwalan Rute Perjalanan Divisi Pemasaran STMIK El Rahma

Teknika ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 110-118
Author(s):  
Herdiesel Santoso ◽  
Rachmad Sanuri
Keyword(s):  
Traveling Salesman Problem ◽  
Time Window ◽  
Travelling Salesman Problem ◽  
Time Windows ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Google Maps

Divisi pemasaran STMIK El Rahma memiliki permasalahan dengan penjadwalan rute kunjungan ketika harus melakukan perjalanan multi destinasi ke sekolah-sekolah untuk melakukan promosi. Perjalanan multi destinasi dengan mempertimbangkan waktu kunjungan merupakan permasalahan Travelling Salesman Problem with Time Windows (TSP-TW). Algoritma Genetika merupakan salah satu metode pencarian yang dapat digunakan untuk memberikan rute perjalanan yang optimal. Rekomendasi yang diberikan tidak hanya mempertimbangkan jarak tetapi juga waktu tempuh didapatkan menggunakan Google Maps API. Skenario pengujian yang dilakukan adalah pengujian banyak generasi optimal, pengujian banyak populasi optimal, pengujian kombinasi probabilitas crossover (Pc) dan proabilitas mutasi (Pm), serta pengujian konsistensi solusi yang dihasilkan Algoritma Genetika. Hasil pengujian menunjukan bahwa jumlah individu terbaik adalah 150 individu dalam satu populasi. Kriteria berhenti jika setelah 127 generasi berturut-turut didapatkan nilai fitness tertinggi yang tidak berubah dan kombinasi probabilitas crossover dan probabilitas mutasi yang paling optimal adalah {0.3 : 0.7}.

scholarly journals A branch-and-cut and MIP-based heuristics for the Prize-Collecting Travelling Salesman Problem

2020 ◽  
Author(s):  
Glaubos Climaco ◽  
Luidi Simonetti ◽  
Isabel Rosseti
Keyword(s):  
Traveling Salesman Problem ◽  
Travelling Salesman Problem ◽  
Branch And Cut ◽  
Traveling Salesman ◽  
Minimum Amount ◽  
Travelling Salesman ◽  
Travel Costs ◽  
Prize Collecting

The Prize Collecting Traveling Salesman Problem (PCTSP) represents a generalization of the well-known Traveling Salesman Problem. The PCTSP can be associated with a salesman that collects a prize in each visited city and pays a penalty for each unvisited city, with travel costs among the cities. The objective is to minimize the sum of the costs of the tour and penalties, while collecting a minimum amount of prize. This paper suggests MIP-based heuristics and a branch-and-cut algorithm to solve the PCTSP. Experiments were conducted with instances of the literature, and the results of our methods turned out to be quite satisfactory.

scholarly journals Multi Integer Linear Programming and the Traveling Salesman Problem

2021 ◽  
Vol 9 (VI) ◽  
pp. 2417-2421
Author(s):  
S. Sathyapriya
Keyword(s):  
Linear Programming ◽  
Traveling Salesman Problem ◽  
Integer Linear Programming ◽  
Travelling Salesman Problem ◽  
Initial Problem ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Integer Problem ◽  
The Traveling Salesman Problem

The Travelling Salesman problem is considered as a binary integer problem. For this problem, several stop variables and subtours are discussed. The stops are generated and the distance between those stops are found, consequently the graphs are drawn. Further the variables are declared and the constraints are framed. Then the initial problem is visualised along with the subtour constraints in order to achieve the required output.

scholarly journals GENETIC ALGORITHM FOR OPTIMIZING DISTRIBUTION WITH ROUTE RESTRICTION CONSTRAINT DUE TO TRAFFIC JAMS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 295-301
Author(s):  
N. Mouttaki ◽  
J. Benhra ◽  
G. Rguiga
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Traveling Salesman Problem ◽  
Travelling Salesman Problem ◽  
Classical Problem ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Simple Method ◽  
Traffic Jams ◽  
Better Than

Abstract. The Travelling Salesman Problem (TSP) is a classical problem in combinatorial optimization that consists of finding the shortest tour through all cities such that the salesman visits each city only one time and returns to the starting city. Genetic algorithm is one of the powerful ways to solve problems of traveling salesman problem TSP. The current genetic algorithm aims to take in consideration the constraints happening during the execution of genetic algorithm, such as traffic jams when solving TSP. This program has two important contributions. First one is proposing simple method into taking in consideration an inconvenient route linked to traffic jams. The second one is the use of closeness strategy during the initialization step, which can accelerate the execution time of the algorithm.The results of the experiments show that the improved algorithm works better than some other algorithms. The conclusion ends the analysis with recommendations and future works.

scholarly journals Comparative Study of Prim and Genetic Algorithms in Minimum Spanning Tree and Travelling Salesman Problem

2018 ◽  
Author(s):  
Andysah Putera Utama Siahaan ◽  
Andre Hasudungan Lubis
Keyword(s):  
Genetic Algorithm ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Travelling Salesman Problem ◽  
Operational Cost ◽  
Travelling Salesman ◽  
Maximum Value ◽  
Large Numbers ◽  
Crossover And Mutation ◽  
Better Than

Optimization is the essential thing in an algorithm. It can save the operational cost of an activity. At the Minimum Spanning Tree, the goal to be achieved is how all nodes are connected with the smallest weights. Several algorithms can calculate the use of weights in this graph. Genetic and Primary algorithms are two very popular algorithms for optimization. Prim calculates the weights based on the short-est distance from a graph. This algorithm eliminates the connected loop to minimize circuit. The nature of this algorithm is to trace all nodes to the smallest weights on a given graph. The genetic algorithm works by determining the random value as first initialization. This algorithm will perform selection, crossover, and mutation by the number of rounds specified. It is possible that this algorithm can not achieve the maximum value. The nature of the genetic algorithm is to work with probability. The results obtained are the most optimal results according to this algorithm. The results of this study indicate that the Prim is better than Genetics in determining the weights at the minimum spanning tree while Genetic algorithm is better for travelling salesman problem. Genetics will have maximum results when using large numbers of rotations and populations.

scholarly journals Genetic Algorithms Dynamic Population Size with Cloning in Solving Traveling Salesman Problem

2018 ◽  
Vol 2 (2) ◽  
pp. 87-100 ◽  
Author(s):  
Erna Budhiarti Nababan ◽  
Opim Salim Sitompul ◽  
Yuni Cancer
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Computational Time ◽  
Local Optimum ◽  
Travelling Salesman ◽  
Cloning Strategy ◽  
Population Sizes ◽  
Dynamic Population

Population size of classical genetic algorithm is determined constantly. Its size remains constant over the run. For more complex problems, larger population sizes need to be avoided from early convergence to produce local optimum. Objective of this research is to evaluate population resizing i.e. dynamic population sizing for Genetic Algorithm (GA) using cloning strategy. We compare performance of proposed method and traditional GA employed to Travelling Salesman Problem (TSP) of A280.tsp taken from TSPLIB. Result shown that GA with dynamic population size exceed computational time of traditional GA.

scholarly journals Travelling Salesman Problem Analysis with Complete Enumeration Method, Branch & Bound and Greedy Heuristic

2021 ◽  
Vol 1 (8) ◽  
pp. 752-756
Author(s):  
Ifham Azizi Surya Syafiin ◽  
Sarah Nur Fatimah ◽  
Muchammad Fauzi
Keyword(s):  
Traveling Salesman Problem ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Greedy Heuristic ◽  
Problem Analysis ◽  
Heuristic Methods ◽  
Travelling Salesman ◽  
Complete Enumeration ◽  
The Traveling Salesman Problem ◽  
The City

PT XYZ as the best and largest Bed Sheet Set company in Indonesia with products such as Bed Covers, Bed Sheets, Pillowcases, Bolsters and Blankets. The Traveling Salesman Problem (TSP) is a problem faced in finding the best route to visit shops that sell products from PT BIG. A visit to the shop is carried out on the condition that each city can only be visited once except the city of origin. The algorithms applied in this TSP problem include the Complete Enumeration, Branch & Bound and Greedy Heuristic methods.

Metaheuristics Approaches for the Travelling Salesman Problem on a Spherical Surface

2021 ◽  
pp. 94-113
Author(s):  
Yusuf Sahin ◽  
Erdal Aydemir ◽  
Kenan Karagul ◽  
Sezai Tokat ◽  
Burhan Oran
Keyword(s):  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Spherical Surface ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Heuristic Methods ◽  
Approximate Algorithms ◽  
Travelling Salesman ◽  
The Traveling Salesman Problem ◽  
Special Case

Traveling salesman problem in which all the vertices are assumed to be on a spherical surface is a special case of the conventional travelling salesman problem. There are exact and approximate algorithms for the travelling salesman problem. As the solution time is a performance parameter in most real-time applications, approximate algorithms always have an important area of research for both researchers and engineers. In this chapter, approximate algorithms based on heuristic methods are considered for the travelling salesman problem on the sphere. Firstly, 28 test instances were newly generated on the unit sphere. Then, using various heuristic methods such as genetic algorithms, ant colony optimization, and fluid genetic algorithms, the initial solutions for solving test instances of the traveling salesman problem are obtained in Matlab®. Then, the initial heuristic solutions are used as input for the 2-opt algorithm. The performances and time complexities of the applied methods are analyzed as a conclusion.

scholarly journals Improved Chicken Swarm Optimization Algorithm to Solve the Travelling Salesman Problem

2018 ◽  
Vol 12 (3) ◽  
pp. 1054
Author(s):  
Fayçal Chebihi ◽  
Mohammed essaid Riffi ◽  
Amine Agharghor ◽  
Soukaina Cherif Bourki Semlali ◽  
Abdelfattah Haily
Keyword(s):  
Local Search ◽  
Optimization Algorithm ◽  
Travelling Salesman Problem ◽  
Search Method ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Swarm Optimization ◽  
Chicken Swarm Optimization ◽  
The Traveling Salesman Problem

<p>This paper proposes a novel discrete bio-inspired chicken swarm optimization algorithm (CSO) to solve the problem of the traveling salesman problem (TSP) which is one of the most known problems used to evaluate the performance of the new metaheuristics. This problem is solved by applying a local search method 2-opt in order to improve the quality of the solutions. The DCSO as a swarm system of the algorithm increases the level of diversification, in the same way the hierarchical order of the chicken swarm and the behaviors of chickens increase the level of intensification. In this contribution, we redefined the basic different operators and operations of the CSO algorithm. The performance of the algorithm is tested on a symmetric TSP benchmark dataset from TSPLIB library. Therefore, the algorithm provides good results in terms of both optimization accuracy and robustness comparing to other metaheuristics.</p>

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