Collaboration methods for ensembles of dispatching rules for the dynamic unrelated machines environment
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- @Article{DURASEVIC:2023:engappai,
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author = "Marko Durasevic and Francisco Javier Gil-Gala and
Lucija Planinic and Domagoj Jakobovic",
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title = "Collaboration methods for ensembles of dispatching
rules for the dynamic unrelated machines environment",
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journal = "Engineering Applications of Artificial Intelligence",
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volume = "122",
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pages = "106096",
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year = "2023",
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ISSN = "0952-1976",
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DOI = "doi:10.1016/j.engappai.2023.106096",
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URL = "https://www.sciencedirect.com/science/article/pii/S0952197623002804",
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keywords = "genetic algorithms, genetic programming, Unrelated
machines environment, Scheduling, Dispatching rules,
Ensembles",
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abstract = "Dynamic scheduling represents an important
combinatorial optimisation problem that often appears
in the real world. The difficulty in solving these
problems arises from their dynamic nature, which limits
the applicability of improvement based metaheuristics.
Dynamic problems are usually solved using dispatching
rules (DRs), which iteratively construct the schedule.
Recently, such heuristics have been constructed using
various hyperheuristic methods, most notably genetic
programming. Although automatically designed DRs
achieve good performance, it is still very difficult to
design a single DR that would perform a good decision
at every decision point. As a remedy, DRs were combined
into ensembles to improve their performance. For that
purpose it is required to define how ensembles are
constructed and how DRs in the ensemble collaborate.
This paper proposes a novel ensemble collaboration
method based on a similar method applied for static
scheduling problems and adapts it for dynamic problems.
The goal is to obtain a collaboration method that
produces better results than standard collaboration
methods. Additionally, the paper investigates the
application of novel ensemble construction methods for
dynamic scheduling. The proposed methods are validated
on dynamic unrelated machines scheduling problem and
compared with existing ensemble construction and
collaboration methods. The obtained results demonstrate
that the proposed collaboration method performs better
than standard ones. Further analyses provide additional
insights into the proposed methods and outline several
potential research directions in the area of
hyper-heuristic ensemble construction",
- }
Genetic Programming entries for
Marko Durasevic
Francisco Javier Gil Gala
Lucija Planinic
Domagoj Jakobovic
Citations