Travelling Salesman Problem

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M Clerc - One of the best experts on this subject based on the ideXlab platform.

  • optimal path for automated drilling operations by a new heuristic approach using particle swarm optimization
    International Journal of Production Research, 2004
    Co-Authors: Godfrey C Onwubolu, M Clerc
    Abstract:

    A new heuristic approach for minimizing the operating path of automated or computer numerically controlled drilling operations is described. The operating path is first defined as a Travelling Salesman Problem. The new heuristic, particle swarm optimization, is then applied to the Travelling Salesman Problem. A model for the approximate prediction of drilling time based on the heuristic solution is presented. The new method requires few control variables: it is versatile, robust and easy to use. In a batch production of a large number of items to be drilled such as in printed circuit boards, the travel time of the drilling device is a significant portion of the overall manufacturing process, hence the new particle swarm optimization–Travelling Salesman Problem heuristic can play a role in reducing production costs.

Dewi, Priska Sari - One of the best experts on this subject based on the ideXlab platform.

  • APLIKASI Travelling Salesman Problem PADA PENGEDROPAN BARANG DI ANJUNGAN MENGGUNAKAN METODE INSERTION
    'Universitas Jenderal Soedirman', 2021
    Co-Authors: Dewi, Priska Sari, Triyani Triyani, Nurshiami, Siti Rahmah
    Abstract:

    Travelling Salesman Problem (TSP) is a Problem to find the shortest path a Salesman visitS all the cities exactly once, and return to the starting city. In this reseacrh, the methods for TSP used are the nearest insertion method, the cheapest insertion method, and the farthest insertion method. With help the function of Software R to creat a minimum TSP Program from three insertion methods.The TSP results for same number of point using three insertion methods do not always have the same weight and route but depending on the data used

  • Aplikasi Travelling Salesman Problem Pada Pengedropan Barang di Anjungan Menggunakan Metode Insertion
    2020
    Co-Authors: Dewi, Priska Sari
    Abstract:

    Travelling Salesman Problem (TSP) merupakan permasalahan mencari lintasan terpendek seorang Salesman harus mengunjungi semua kota yang akan dituju tepat sekali, dan kembali ke kota awal. Pada penelitian ini, penyelesaian TSP menggunakan metode nearest insertion, metode cheapest insertion, dan metode farthest insertion. Bantuan fungsi Software R digunakan untuk membuat program minimum TSP dari ketiga metode. Hasil TSP untuk jumlah titik yang sama dengan menggunakan tiga metode insertion tidak selalu menghasilkan bobot dan rute lintasan yang sama namun bergantung pada data yang digunakan

Sarat Tangudu - One of the best experts on this subject based on the ideXlab platform.

  • a hybrid random key genetic algorithm for a symmetric Travelling Salesman Problem
    International Journal of Operational Research, 2007
    Co-Authors: Funda Samanlioglu, Mary Beth Kurz, William G Ferrell, Sarat Tangudu
    Abstract:

    This paper describes a methodology that finds approximate and sometimes optimal solutions to the symmetric Travelling Salesman Problem (TSP) using a hybrid approach that combines a Random-Key Genetic Algorithm (RKGA) with a local search procedure. The random keys representation ensures that feasible tours are constructed during the application of genetic operators, whereas the genetic algorithm approach with local search efficiently generates optimal or near-optimal solutions. The results of experiments are provided that use examples taken from a well-known online library to confirm the quality of the proposed algorithm.

Godfrey C Onwubolu - One of the best experts on this subject based on the ideXlab platform.

  • optimal path for automated drilling operations by a new heuristic approach using particle swarm optimization
    International Journal of Production Research, 2004
    Co-Authors: Godfrey C Onwubolu, M Clerc
    Abstract:

    A new heuristic approach for minimizing the operating path of automated or computer numerically controlled drilling operations is described. The operating path is first defined as a Travelling Salesman Problem. The new heuristic, particle swarm optimization, is then applied to the Travelling Salesman Problem. A model for the approximate prediction of drilling time based on the heuristic solution is presented. The new method requires few control variables: it is versatile, robust and easy to use. In a batch production of a large number of items to be drilled such as in printed circuit boards, the travel time of the drilling device is a significant portion of the overall manufacturing process, hence the new particle swarm optimization–Travelling Salesman Problem heuristic can play a role in reducing production costs.

Revika Fara Maylinda 081411233016 - One of the best experts on this subject based on the ideXlab platform.

  • DYNAMIC Travelling Salesman Problem (DTSP)MENGGUNAKAN HYBRID ALGORITMA PARTICLE SWARM OPTIMIZATION (PSO) DAN GENETIC ALGORITHM (GA)
    2018
    Co-Authors: Revika Fara Maylinda 081411233016
    Abstract:

    Dynamic Travelling Salesman Problem (DTSP) akan diselesakan dengan hybrid Particle Swarm Optimization (PSO) dan Genetic Algorithm (GA). DTSP merupakan permasalahan dimana seorang sales harus melalui semua kota yang sudah ditetapkan dan setiap kota hanya boleh dilewati satu kali. Tujuannya adalah mencari rute terpendek untuk melewati sejumlah kota tersebut, dan perjalanan diakhiri dengan kembali ke kota semula dimana terdapat pengurangan atau penambahan kota tujuan sebelum perjalanan berakhir. Hybrid Particle Swarm Optimization dan Genetic Algorithm adalah menggabungkan proses algoritma Genetika dengan PSO, proses algoritma genetika dilakukan pertama kali, output dari algoritma ini diproses dengan algoritma PSO. Secara umum proses Algoritma Genetika adalah inisialisasi parameter, pembangkitan populasi awal, evalusi fungsi tujuan, seleksi, crossover, dan mutasi. Dalam tugas akhir ini, proses seleksi yang digunakan adalah Roulette Wheel, proses crossover yang digunakan adalah Two Cut Point Crossover, dan proses mutasi yang digunakan adalah Reciprocal Exchange. Kemudian dilanjutkan proses PSO adalah pemilihan partikel awal, pembangkitan kecepatan awal, pemilihan pbest dang best, update kecepatan dan partikel, evaluasi fungsi tujuan, dan proses berlanjut sampai maksimal iterasi. Ada 3 jenis data yang digunakan dan diselesaikan dengan progam C ++ yang dibuat oleh perangkat lunak Borland C ++. Hasil perhitungan jarak tempuh total minimal 10 kota setelah terdapat penambahan 5 kota tujuan adalah 316, untuk perhitungan 25 kota setelah terdapat penambahkan 5 kota tujuan adalah 10422, sedangkan untuk perhitungan 100 kota setelah penambahan terdapat 5 kota tujuan adalah 60172. Perubahan nilai parameter dapat mempengaruhi hasil. Semakin besar jumlah kromosom dan Pc serta banyak iterasi cenderung memberikan hasil yang lebih baik

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