Job Shop Scheduling Problem menggunakan Ant Colony Optimization dan Algoritme Genetika

Rizka Aulia; Shinta Aprilisa

doi:10.31004/jutin.v7i3.31591

Authors

Rizka Aulia Universitas PGRI Silampari
Shinta Aprilisa Universitas PGRI Silampari

DOI:

https://doi.org/10.31004/jutin.v7i3.31591

Keywords:

Ant Colony Optimization, Genetic Algorithm, Job Shop Scheduling Problem

Abstract

Problem (JSSP) is a problem to determine the sequence of operations carried out on existing machines with the aim of minimizing the total processing time required. The development of optimization methods to achieve solutions to machine operation sequence problems has encouraged the emergence of many new solution methods. This research wants to compare two solution methods using Ant Colony Optimization (ACO) and Genetic Algorithms. The two methods are compared to find out which optimization is best used to solve the JSSP problem. The results of this research show that the ACO algorithm is better with mean squared error of 72.99%, compared to the Genetic Algorithm with mean squared error of 11.71%.

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