scholarly journals OPTIMALISASI PENDISTRIBUSIAN DENGAN METODE TRAVELLING SALESMAN PROBLEM UNTUK MENENTUKAN RUTE TERPENDEK DI PT XYZ

2021 ◽  
Vol 16 (2) ◽  
pp. 84-95
Author(s):  
Muhammad Viqri Ardiansyah ◽  
Rizki Achmad Darajatun ◽  
Dimas Nurwinata Rinaldi
Keyword(s):  
Branch And Bound ◽  
Nearest Neighbor ◽  
Travelling Salesman Problem ◽  
Travelling Salesman ◽  
Insertion Heuristic

Optimalisasi rute pendistribusian menjadi salah satu target utama perusahaan dalam pendistribusian setiap produknya. Hal tersebut bertujuan untuk mendapatkan jarak pendistribusian optimal, minimasi biaya bahan bakar, dan waktu pengiriman yang lebih cepat. Travelling Salesman Problem (TSP) menjadi salah satu masalah yang melibatkan optimalisasi proses pendistribusian produk. Dalam penelitian ini, permasalahan TSP digunakan untuk mendapatkan rute pendistribusian optimal pada PT XYZ. Metode TSP yang digunakan sebagai perbandingan adalah Branch and Bound, Nearest Neighbor, Cheapest Insertion Heuristic, dan Two-Ways Exchange Improvement. Dari hasil yang didapat menggunakan WinQSB, didapat bahwa keempat metode TSP tersebut dapat meminimalkan rute pendistribusian, sehingga biaya bahan bakar juga dapat menurun. Namun rute yang memiliki jarak terpendek berasal dari metode Two-Ways Exchange Improvement dengan selisih jarak pendistribusian sebesar 16,78 KM dan biaya bahan bakar sebesar Rp. 219.410.

scholarly journals A Genetic Method using Hybrid Crossover for Solving Travelling Salesman Problem

2019 ◽  
Vol 8 (2) ◽  
pp. 5066-5072
Keyword(s):  
Hybrid Method ◽  
Nearest Neighbor ◽  
Travelling Salesman Problem ◽  
Performance Comparison ◽  
Initial Population ◽  
Genetic Approach ◽  
Comparison Standard ◽  
Travelling Salesman ◽  
Genetic Method ◽  
Better Than

This paper proposes a Genetic approach using Hybrid Crossover for Solving the Travelling Salesman Problem. Proposed hybrid method generates an initial population using Nearest Neighbor (NN) approach which is modified using “Sub-Path Mutation” (SPM) process. Modified population undergoes Distance Preserving Crossover (DPX) [2] and 2-opt Optimal mutation (2-opt) [1] to check for possible refinement. SPM searches position for the minimum distant city within a given path. This work is motivated by the algorithm developed by [3] who performed DPX and 2-opt mutation on the initial population generated using NN. For performance comparison, standard TSPLIB data is taken. The proposed hybrid method performances better in terms of % best error. It performs better than methods reported in [3 - 11].

scholarly journals The Cluster Crossover Operation for The Symmetric Travelling Salesman Problem

2019 ◽  
Vol 12 (2) ◽  
pp. 98-105
Author(s):  
Ajchara Phu-ang ◽  
Duangjai Jitkongchuen
Keyword(s):  
Local Search ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Travelling Salesman Problem ◽  
Hill Climbing ◽  
Search Process ◽  
Initial Population ◽  
Travelling Salesman ◽  
Average Percentage ◽  
Surrounding Areas

This paper proposed the new algorithm intended to solve a specific real-world problem, the symmetric travelling salesman problem. The proposed algorithm is based on the concept of the galaxy based search algorithm (GbSA) and  embedded the new ideas called the clockwise search process and the cluster crossover operation. In the first step, the nearest neighbor algorithm introduces to generate the initial population. Then, the tabu list local search is employed to search for the new solution in surrounding areas of the initial population in the second step. The clockwise search process and the cluster crossover operation are employed to create more diversity of the new solution. Then, the final step, the hill climbing local search is utilized to increase the local search capabilities. The experiments with the standard benchmark test sets show that the proposed algorithm can be found the best average percentage deviation from the lower bound.

scholarly journals Penerapan Bat Algorithm Dalam Penyelsaian Kasus Travelling Salesman Problem (TSP) Pada Internship Program

2021 ◽  
Vol 6 (2) ◽  
pp. 111-116
Author(s):  
Veri Julianto ◽  
Hendrik Setyo Utomo ◽  
Muhammad Rusyadi Arrahimi
Keyword(s):  
Global Minimum ◽  
Nearest Neighbor ◽  
Travelling Salesman Problem ◽  
Bat Algorithm ◽  
Travelling Salesman ◽  
Street Vendors ◽  
Shortest Distance ◽  
Using Data ◽  
Feasible Solutions ◽  
The Traveling Salesman Problem

This optimization is an optimization case that organizes all possible and feasible solutions in discrete form. One form of combinatorial optimization that can be used as material in testing a method is the Traveling Salesman Problem (TSP). In this study, the bat algorithm will be used to find the optimum value in TSP. Utilization of the Metaheuristic Algorithm through the concept of the Bat Algorithm is able to provide optimal results in searching for the shortest distance in the case of TSP. Based on trials conducted using data on the location of student street vendors, the Bat Algortima is able to obtain the global minimum or the shortest distance when compared to the nearest neighbor method, Hungarian method, branch and bound method.

scholarly journals Lifting the Performance of a Heuristic for the Time-Dependent Travelling Salesman Problem through Machine Learning

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 340
Author(s):  
Gianpaolo Ghiani ◽  
Tommaso Adamo ◽  
Pierpaolo Greco ◽  
Emanuela Guerriero
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Travelling Salesman Problem ◽  
Computing Time ◽  
Time Dependent ◽  
Machine Learning Techniques ◽  
Travelling Salesman ◽  
Distribution Management ◽  
Learning Techniques ◽  
Average Improvement

In recent years, there have been several attempts to use machine learning techniques to improve the performance of exact and approximate optimization algorithms. Along this line of research, the present paper shows how supervised and unsupervised techniques can be used to improve the quality of the solutions generated by a heuristic for the Time-Dependent Travelling Salesman Problem with no increased computing time. This can be useful in a real-time setting where a speed update (or the arrival of a new customer request) may lead to the reoptimization of the planned route. The main contribution of this work is to show how to reuse the information gained in those settings in which instances with similar features have to be solved over and over again, as it is customary in distribution management. We use a method based on the nearest neighbor procedure (supervised learning) and the K-means algorithm with the Euclidean distance (unsupervised learning). In order to show the effectiveness of this approach, the computational experiments have been carried out for the dataset generated based on the real travel time functions of two European cities: Paris and London. The overall average improvement of our heuristic over the classical nearest neighbor procedure is about 5% for London, and about 4% for Paris.

scholarly journals PENERAPAN ALGORITMA BRANCH AND BOUND PADA PERSOALAN PEDAGANG KELILING (TRAVELLING SALESMAN PROBLEM)

2020 ◽  
Vol 1 (2) ◽  
pp. 111-121
Author(s):  
Justin Eduardo Simarmata
Keyword(s):  
Branch And Bound ◽  
Travelling Salesman Problem ◽  
Travelling Salesman

Persoalan pedagang keliling merupakan persoalan optimasi untuk mencari perjalanan terpendek bagi pedagang keliling yang ingin berkunjung ke beberapa kota, dan kembali ke kota asal keberangkatan. Beberapa metode telah digunakan untuk memecahkan  persoalan TSP. Namun, pada zaman yang serba praktis sekarang ini dibutuhkan algoritma yang dapat menyelesaikan TSP dengan cepat sehingga diperoleh solusi yang mendekati.  Penelitian ini membahas tentang algoritma Branch and Bound dalam menyelesaikan persoalan TSP. Dengan menerapkan algoritma Branch and Bound pada hasil pembahasan persoalan pedagang keliling (Travelling Salesman Problem) dalam penelitian ini maka diperoleh rute perjalanan terpendek dengan biaya yang paling minimal.

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