IMPLEMENTASI TRAVELING SALESMAN PROBLEM (TSP) DENGAN ANT COLONY OPTIMIZATION (ACO) DAN PARTICLE SWARM OPTIMIZATION (PSO) PADA PENDISTRIBUSIAN PUPUK PT. AUSTINDO NUSANTARA JAYA Tbk.

NAIBAHO, MANGARA TUA and Indrawati, Indrawati and Bangun, Putra Bahtera Jaya (2018) IMPLEMENTASI TRAVELING SALESMAN PROBLEM (TSP) DENGAN ANT COLONY OPTIMIZATION (ACO) DAN PARTICLE SWARM OPTIMIZATION (PSO) PADA PENDISTRIBUSIAN PUPUK PT. AUSTINDO NUSANTARA JAYA Tbk. Undergraduate thesis, Sriwijaya University.

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Abstract

The problem of Traveling Salesman Problem is one of the optimization problems of the Hamiltonian circuit, to find the shortest route a salesman must travel to a number of cities exactly once and return to the city early. There are many algorithms that can be used to find the shortest route such as Ant Colony Optimization Algorithm (ACO) and Particle Swarm Optimization (PSO) algorithm. This study aims to apply the ACO and PSO algorithm to solve the problem of finding the shortest route of distribution of fertilizer PT. Sahabat Mewah Makmur (SMM). In distributing fertilizer, PT.Sahabat Mewah Makmur (SMM) distributes fertilizer from Tanjung Pandan to the warehouse location in Jangkang, Balok, Ladang Jaya, Sari Bunga, and Aik Ruak. In the ACO, we obtained the shortest route of distribution of fertilizer PT.SMM is port of Tanjung Pandan - Jangkang - Balok - Aik Ruak – Ladang Jaya - Sari Bunga – port of Tanjung Pandan, with the distance of 175 km. In the PSO, we obtained the shortest route is port of Tanjung Pandan - Jangkang - Ladang Jaya -Sari Bunga - Aik Ruak - Balok – port of Tanjung Pandan, with the distance of 175 km. The solutions obtained from Ant Colony Optimization (ACO) and Genetic Algorithms (GA) are better than Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms, but the solution obtained from Simulated Annealing (SA) is better than the Particle Swarm Optimization (PSO) algorithm for the same case.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Traveling Salesman Problem, Ant Colony Optimization, Particle Swarm Optimization
Subjects: Q Science > QA Mathematics > QA1-939 Mathematics
Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics
Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions: 08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User: Mrs Dies Meirita Sari
Date Deposited: 02 Aug 2019 07:26
Last Modified: 02 Aug 2019 07:26
URI: http://repository.unsri.ac.id/id/eprint/1932

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