Paper Info
Title
Evolving dispatching rules for optimising many-objective criteria in the unrelated machines environment.
Abstract
Dispatching rules are often a method of choice for solving various scheduling problems. Most often, they are designed by human experts in order to optimise a certain criterion. However, it is seldom the case that a schedule should optimise a single criterion all alone. More common is the case where several criteria need to be optimised at the same time. This paper deals with the problem of automatic design of dispatching rules (DRs) by the use of genetic programming, for many-objective scheduling problems. Four multi-objective and many-objective algorithms, including nondominated sorting genetic algorithm II, nondominated sorting genetic algorithm III, harmonic distance based multi-objective evolutionary algorithm and multi-objective evolutionary algorithm based on decomposition, have been used in order to obtain sets of Pareto optimal solutions for various many-objective scheduling problems. Through experiments it was shown that automatically generated multi-objective DRs not only achieve good performance when compared to standard DRs, but can also outperform automatically generated single objective DRs for most criteria combinations.
Year
DOI
Venue
2018
https://doi.org/10.1007/s10710-017-9310-3
Genetic Programming and Evolvable Machines
Keywords
Field
DocType
Dispatching rules,Genetic programming,Many-objective optimisation,Scheduling,Unrelated machines environment
Mathematical optimization,Evolutionary algorithm,Scheduling (computing),Computer science,Harmonic,Pareto optimal,Genetic programming,Sorting,Artificial intelligence,Single objective,Genetic algorithm,Machine learning
Journal
Volume
Issue
ISSN
19
1-2
1389-2576
Citations 
PageRank 
References 
2
0.37
39
Authors
2
Name
Order
Citations
PageRank
Marko Durasevic191.48
Domagoj Jakobovic219529.01