Evolving dispatching rules for optimising many-objective criteria in the unrelated machines environment

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...

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Bibliographic Details
Published inGenetic programming and evolvable machines Vol. 19; no. 1-2; pp. 9 - 51
Main Authors Ɖurasević, Marko, Jakobović, Domagoj
Format Journal Article
LanguageEnglish
Published New York Springer US 01.06.2018
Springer Nature B.V
Subjects
Artificial Intelligence
Biomedical Engineering and Bioengineering
Classification
Compilers
Computer Science
Criteria
Dispatching rules
Electrical Engineering
Evolutionary algorithms
Genetic algorithms
Interpreters
Multiple objective analysis
Optimization
Production scheduling
Programming Languages
Programming Techniques
Scheduling
Software Engineering/Programming and Operating Systems
Sorting algorithms
Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation
Scheduling
Dispatching rules
Genetic programming
Many-objective optimisation
Unrelated machines environment
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ISSN1389-2576
1573-7632
DOI10.1007/s10710-017-9310-3

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Summary: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.
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ISSN:1389-2576
1573-7632
DOI:10.1007/s10710-017-9310-3