Abstract
The multiobjective job shop scheduling problem with mean tardiness and the maximum tardiness as the objectives is addressed. A genetic local search algorithm is proposed with several features. First, it uses a dispatching rule-based genome encoding scheme, and the encoded dispatching rules are chosen carefully. Second, its mating selection mechanism combines the advantages of two representative ones in the literature. Third, it enhances a recently proposed population-based local search procedure. The benefits of the proposed idea are verified through experiments on a public benchmark problem set. In the experiments, the proposed algorithm is also shown to significantly outperform a recent algorithm specific to the multiobjective job shop scheduling problem.
| Original language | English |
|---|---|
| Title of host publication | 36th International Conference on Computers and Industrial Engineering, ICC and IE 2006 |
| Pages | 1764-1775 |
| Number of pages | 12 |
| Publication status | Published - 2006 |
| Externally published | Yes |
| Event | 36th International Conference on Computers and Industrial Engineering, ICC and IE 2006 - Taipei, Taiwan Duration: 2006 Jun 20 → 2006 Jun 23 |
Other
| Other | 36th International Conference on Computers and Industrial Engineering, ICC and IE 2006 |
|---|---|
| Country | Taiwan |
| City | Taipei |
| Period | 06/6/20 → 06/6/23 |
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Keywords
- Dispatching rules
- Job shop scheduling
- Multiobjective evolutionary algorithms
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
Cite this
Multiobjective job shop scheduling using rule-coded genetic local search. / Chiang, Tsung Che; Fu, Li Chen.
36th International Conference on Computers and Industrial Engineering, ICC and IE 2006. 2006. p. 1764-1775.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multiobjective job shop scheduling using rule-coded genetic local search
AU - Chiang, Tsung Che
AU - Fu, Li Chen
PY - 2006
Y1 - 2006
N2 - The multiobjective job shop scheduling problem with mean tardiness and the maximum tardiness as the objectives is addressed. A genetic local search algorithm is proposed with several features. First, it uses a dispatching rule-based genome encoding scheme, and the encoded dispatching rules are chosen carefully. Second, its mating selection mechanism combines the advantages of two representative ones in the literature. Third, it enhances a recently proposed population-based local search procedure. The benefits of the proposed idea are verified through experiments on a public benchmark problem set. In the experiments, the proposed algorithm is also shown to significantly outperform a recent algorithm specific to the multiobjective job shop scheduling problem.
AB - The multiobjective job shop scheduling problem with mean tardiness and the maximum tardiness as the objectives is addressed. A genetic local search algorithm is proposed with several features. First, it uses a dispatching rule-based genome encoding scheme, and the encoded dispatching rules are chosen carefully. Second, its mating selection mechanism combines the advantages of two representative ones in the literature. Third, it enhances a recently proposed population-based local search procedure. The benefits of the proposed idea are verified through experiments on a public benchmark problem set. In the experiments, the proposed algorithm is also shown to significantly outperform a recent algorithm specific to the multiobjective job shop scheduling problem.
KW - Dispatching rules
KW - Job shop scheduling
KW - Multiobjective evolutionary algorithms
UR - http://www.scopus.com/inward/record.url?scp=55749095208&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=55749095208&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:55749095208
SP - 1764
EP - 1775
BT - 36th International Conference on Computers and Industrial Engineering, ICC and IE 2006
ER -