<div class="csl-bib-body">
<div class="csl-entry">Mischek, F., & Musliu, N. (2023). Leveraging problem-independent hyper-heuristics for real-world test laboratory scheduling. In <i>GECCO ’23: Proceedings of the Genetic and Evolutionary Computation Conference</i> (pp. 321–329). Association for Computing Machinery (ACM). https://doi.org/10.1145/3583131.3590354</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/193566
-
dc.description.abstract
The area of project scheduling problems has seen a tremendous amount of different problem variations. Traditionally, each problem variant requires custom solution approaches in order to produce high-quality solutions. Developing and tuning these methods is an expensive process that may have to be repeated as soon as the requirements or problem structures change. On the other hand, research into hyper-heuristics has produced general heuristic problem-solving techniques that were developed to achieve good results on multiple diverse problem domains. They work with a set of comparatively simple low-level heuristics and dynamically adapt themselves to each new problem variant. In this paper, we investigate hyper-heuristic approaches for a real-world industrial test laboratory scheduling problem and develop a new problem domain for the HyFlex hyper-heuristic framework. We propose a diverse portfolio of low-level heuristics that can be dynamically selected during the search process by hyper-heuristics to solve the problem. We evaluate and compare the performance of several problem-independent hyper-heuristics on this domain and show that they are able to match, and sometimes even exceed, the performance of state-of-The-Art solution techniques that were developed and tuned specifically for this problem.
en
dc.description.sponsorship
Christian Doppler Forschungsgesells
-
dc.language.iso
en
-
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
-
dc.subject
HyFlex
en
dc.subject
hyper-heuristics
en
dc.subject
test laboratory scheduling
en
dc.subject
EVALUATION
en
dc.subject
low-level heuristics
en
dc.subject
problem variations
en
dc.subject
high-quality solutions
en
dc.subject
Developing
en
dc.subject
general heuristic
en
dc.subject
real-world industrial test
en
dc.title
Leveraging problem-independent hyper-heuristics for real-world test laboratory scheduling
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.relation.isbn
979-8-4007-0119-1
-
dc.description.startpage
321
-
dc.description.endpage
329
-
dc.relation.grantno
keine Angabe
-
dc.rights.holder
(c) 2023 Copyright held by the owner/author(s).
-
dc.type.category
Full-Paper Contribution
-
tuw.booktitle
GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference
-
tuw.peerreviewed
true
-
tuw.relation.publisher
Association for Computing Machinery (ACM)
-
tuw.relation.publisherplace
New York
-
tuw.project.title
CD Labor für Künstliche Intelligenz und Optimierung in Planung und Scheduling
-
tuw.researchTopic.id
I1
-
tuw.researchTopic.name
Logic and Computation
-
tuw.researchTopic.value
100
-
tuw.publication.orgunit
E192-02 - Forschungsbereich Databases and Artificial Intelligence
-
tuw.publisher.doi
10.1145/3583131.3590354
-
dc.description.numberOfPages
9
-
tuw.author.orcid
0000-0003-1166-3881
-
tuw.author.orcid
0000-0002-3992-8637
-
dc.rights.identifier
CC BY 4.0
de
dc.rights.identifier
CC BY 4.0
en
tuw.event.name
GECCO 2023: Genetic and Evolutionary Computation Conference
-
tuw.event.startdate
15-07-2023
-
tuw.event.enddate
19-07-2023
-
tuw.event.online
Hybrid
-
tuw.event.type
Event for scientific audience
-
tuw.event.place
Lisbon
-
tuw.event.country
PT
-
tuw.event.presenter
Mischek, Florian
-
tuw.event.track
Multi Track
-
wb.sciencebranch
Informatik
-
wb.sciencebranch
Mathematik
-
wb.sciencebranch.oefos
1020
-
wb.sciencebranch.oefos
1010
-
wb.sciencebranch.value
80
-
wb.sciencebranch.value
20
-
item.openaccessfulltext
Open Access
-
item.grantfulltext
open
-
item.cerifentitytype
Publications
-
item.mimetype
application/pdf
-
item.openairecristype
http://purl.org/coar/resource_type/c_5794
-
item.languageiso639-1
en
-
item.openairetype
conference paper
-
item.fulltext
with Fulltext
-
crisitem.project.funder
Christian Doppler Forschungsgesells
-
crisitem.project.grantno
keine Angabe
-
crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence
-
crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence