• Genetic Algorithm and Particle...

Treffer: Genetic Algorithm and Particle Swarm Optimization Techniques for Solving Multi-Objectives on Single Machine Scheduling Problem

Title:
Genetic Algorithm and Particle Swarm Optimization Techniques for Solving Multi-Objectives on Single Machine Scheduling Problem
Authors:
Alaa Sabah Hameed, Hanan Ali Chachan
Source:
Ibn Al-Haitham Journal for Pure and Applied Sciences, Vol 33, Iss 1 (2020)
Publisher Information:
University of Baghdad
Publication Year:
2020
Collection:
Directory of Open Access Journals: DOAJ Articles
Subject Terms:
The Branch and Bound method (BAB), The Local search algorithms, The Genetic Algorithm (GA), The Particle swarm optimization (PSO), The Multi-Objective problems, Science
Document Type:
Fachzeitschrift article in journal/newspaper
Language:
English
Relation:
https://jih.uobaghdad.edu.iq/index.php/j/article/view/2378; https://doaj.org/toc/1609-4042; https://doaj.org/toc/2521-3407; https://doaj.org/article/57aa8b7d432d4610b7b176035a98ee16
DOI:
10.30526/33.1.2378
Availability:
https://doi.org/10.30526/33.1.2378
https://doaj.org/article/57aa8b7d432d4610b7b176035a98ee16
Accession Number:
edsbas.D3A71E83
Database:
BASE

Weitere Informationen

In this paper, two of the local search algorithms are used (genetic algorithm and particle swarm optimization), in scheduling number of products (n jobs) on a single machine to minimize a multi-objective function which is denoted as (total completion time, total tardiness, total earliness and the total late work). A branch and bound (BAB) method is used for comparing the results for (n) jobs starting from (5-18). The results show that the two algorithms have found the optimal and near optimal solutions in an appropriate times.