Details
| Original language | English |
|---|---|
| Pages (from-to) | 51-56 |
| Number of pages | 6 |
| Journal | Procedia CIRP |
| Volume | 96 |
| Early online date | 10 Feb 2021 |
| Publication status | Published - 2021 |
| Event | 8th CIRP Global Web Conference on Flexible Mass Customisation, CIRPe 2020 - Leuven, Belgium Duration: 14 Oct 2020 → 16 Oct 2020 Conference number: 8 |
Abstract
Higher customer requirements and increasing competitive pressure lead to a growing number of variants and smaller batch sizes in manufacturing. As a result, companies have to deal with challenges of small series manufacturing. A unidimensional set-up optimization cannot provide a satisfying solution. At the same time, production system-specific control algorithms are usually complex and require constant adaptation as customer requirements change continuously. To address these issues, this paper presents a hybrid genetic algorithm which allows modular sequence optimization in production scheduling. Hybrid elements are used to reach a high solution quality within a short runtime.
Keywords
- adaptivity, Flowshop, hybrid genetic algorithm, modular design, scheduling, Sequencing
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 96, 2021, p. 51-56.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Modular sequence optimization with hybrid genetic algorithm
AU - Denkena, B.
AU - Dittrich, M. A.
AU - Wilmsmeier, S.
AU - Settnik, S. J.
N1 - Conference code: 8
PY - 2021
Y1 - 2021
N2 - Higher customer requirements and increasing competitive pressure lead to a growing number of variants and smaller batch sizes in manufacturing. As a result, companies have to deal with challenges of small series manufacturing. A unidimensional set-up optimization cannot provide a satisfying solution. At the same time, production system-specific control algorithms are usually complex and require constant adaptation as customer requirements change continuously. To address these issues, this paper presents a hybrid genetic algorithm which allows modular sequence optimization in production scheduling. Hybrid elements are used to reach a high solution quality within a short runtime.
AB - Higher customer requirements and increasing competitive pressure lead to a growing number of variants and smaller batch sizes in manufacturing. As a result, companies have to deal with challenges of small series manufacturing. A unidimensional set-up optimization cannot provide a satisfying solution. At the same time, production system-specific control algorithms are usually complex and require constant adaptation as customer requirements change continuously. To address these issues, this paper presents a hybrid genetic algorithm which allows modular sequence optimization in production scheduling. Hybrid elements are used to reach a high solution quality within a short runtime.
KW - adaptivity
KW - Flowshop
KW - hybrid genetic algorithm
KW - modular design
KW - scheduling
KW - Sequencing
UR - http://www.scopus.com/inward/record.url?scp=85101174007&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2021.01.052
DO - 10.1016/j.procir.2021.01.052
M3 - Conference article
AN - SCOPUS:85101174007
VL - 96
SP - 51
EP - 56
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 8th CIRP Global Web Conference on Flexible Mass Customisation, CIRPe 2020
Y2 - 14 October 2020 through 16 October 2020
ER -