Abstract
The multiobjective flowshop problem with makespan and total flow time as objectives is addressed. A genetic local search algorithm is proposed with the ability to allocate the computational resources through the dynamic population size and local search intensity. The proposed method is compared with existing algorithms for flowshop scheduling with a public benchmark problem set. The experimental results show that the proposed method is capable of discovering solutions with better quality and diversity. The proposed method yields the best known nondominated solutions for the commonly studied permutation flowshop benchmarks, and the set of best known solutions is useful for the evaluation of performance of future studies.
| Original language | English |
|---|---|
| Title of host publication | 2008 IEEE Congress on Evolutionary Computation, CEC 2008 |
| Pages | 1596-1602 |
| Number of pages | 7 |
| DOIs | |
| Publication status | Published - 2008 Nov 14 |
| Event | 2008 IEEE Congress on Evolutionary Computation, CEC 2008 - Hong Kong, China Duration: 2008 Jun 1 → 2008 Jun 6 |
Other
| Other | 2008 IEEE Congress on Evolutionary Computation, CEC 2008 |
|---|---|
| Country | China |
| City | Hong Kong |
| Period | 08/6/1 → 08/6/6 |
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ASJC Scopus subject areas
- Computational Theory and Mathematics
- Theoretical Computer Science
Cite this
Multiobjective permutation flowshop scheduling by an adaptive genetic local search algorithm. / Cheng, Hsueh Chien; Chiang, Tsung-Che; Fu, Li Chen.
2008 IEEE Congress on Evolutionary Computation, CEC 2008. 2008. p. 1596-1602 4631005.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multiobjective permutation flowshop scheduling by an adaptive genetic local search algorithm
AU - Cheng, Hsueh Chien
AU - Chiang, Tsung-Che
AU - Fu, Li Chen
PY - 2008/11/14
Y1 - 2008/11/14
N2 - The multiobjective flowshop problem with makespan and total flow time as objectives is addressed. A genetic local search algorithm is proposed with the ability to allocate the computational resources through the dynamic population size and local search intensity. The proposed method is compared with existing algorithms for flowshop scheduling with a public benchmark problem set. The experimental results show that the proposed method is capable of discovering solutions with better quality and diversity. The proposed method yields the best known nondominated solutions for the commonly studied permutation flowshop benchmarks, and the set of best known solutions is useful for the evaluation of performance of future studies.
AB - The multiobjective flowshop problem with makespan and total flow time as objectives is addressed. A genetic local search algorithm is proposed with the ability to allocate the computational resources through the dynamic population size and local search intensity. The proposed method is compared with existing algorithms for flowshop scheduling with a public benchmark problem set. The experimental results show that the proposed method is capable of discovering solutions with better quality and diversity. The proposed method yields the best known nondominated solutions for the commonly studied permutation flowshop benchmarks, and the set of best known solutions is useful for the evaluation of performance of future studies.
UR - http://www.scopus.com/inward/record.url?scp=55749104384&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=55749104384&partnerID=8YFLogxK
U2 - 10.1109/CEC.2008.4631005
DO - 10.1109/CEC.2008.4631005
M3 - Conference contribution
AN - SCOPUS:55749104384
SN - 9781424418237
SP - 1596
EP - 1602
BT - 2008 IEEE Congress on Evolutionary Computation, CEC 2008
ER -