Year: 2024
Type: article
Abstract: This article investigates a production scheduling task, the Flow Shop Scheduling (FSS) problem. The article solves the FSS task, during which a given number of tasks must be executed on a given number of machines, with a Genetic Algorithm (GA). The Genetic Algorithm is a population-based metaheuristic algorithm that maintains a population of solutions. It performs operations such as mutation and crossover on the current solutions until the stopping condition is not met. The article presents the effectiveness of the Genetic Algorithm on a benchmark data set, compared with six heuristic algorithms. The running results show that the Genetic Algorithm gave the best results in most of the test results.
Source: Multidiszciplináris Tudományok
Author: Anita Agárdi
Institution: University of Miskolc
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405606571