Comparisons of metaheuristic algorithms for unrelated parallel machine weighted earliness/tardiness scheduling problems

This paper investigates unrelated parallel machine scheduling problems where the objectives are to minimize total weighted sum of earliness/tardiness costs. Three different metaheuristic algorithms are compared with others to determine what kind (swarm intelligence based, evolutionary or single solu...

Full description

Saved in:
Bibliographic Details
Published in:Evolutionary intelligence 2020-09, Vol.13 (3), p.415-425
Main Author: Arık, Oğuzhan Ahmet
Format: Article
Language:English
Subjects:
Applications of Mathematics
Artificial Intelligence
Bioinformatics
Computer simulation
Control
Engineering
Evolutionary algorithms
Genetic algorithms
Heuristic methods
Job shops
Mathematical and Computational Engineering
Mechatronics
Research Paper
Robotics
Scheduling
Simulated annealing
Statistical Physics and Dynamical Systems
Swarm intelligence
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper investigates unrelated parallel machine scheduling problems where the objectives are to minimize total weighted sum of earliness/tardiness costs. Three different metaheuristic algorithms are compared with others to determine what kind (swarm intelligence based, evolutionary or single solution) of metaheuristics is effective to solve these problems. In this study, artificial bee colony (ABC), genetic algorithm and simulated annealing algorithm are chosen as swarm intelligence based algorithm, evolutionary algorithm and single solution algorithm. All proposed algorithms are created without modification in order to determine effectiveness of these metaheuristics. Experimental results show that ABC outperforms its opponents in view of solution quality as swarm intelligence based metaheuristic algorithm.
ISSN:1864-5909
1864-5917
DOI:10.1007/s12065-019-00305-7