Abstract
Unrelated parallel machine scheduling under time-of-use electricity price is addressed in this paper. In this setting, price of electricity can be different among various periods of the day. The objective is to minimize total cost consisting of makespan cost and electricity cost. Genetic algorithm (GA) is used to solve the unrelated parallel machine scheduling under time varying tariffs. Chromosome decoding, inspired by greedy total cost, is proposed to transform individual into feasible schedule. Furthermore, generated schedule from the individual is improved by job delay mechanism that shifts jobs to other periods to avoid high electricity cost. Finally, numerical experiment is conducted to implement the approach. Preliminary result shows that our proposed approach is effective and efficient to solve the corresponding problem.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 |
| Publisher | IEEE Computer Society |
| Pages | 583-587 |
| Number of pages | 5 |
| Volume | 2017-December |
| ISBN (Electronic) | 9781538609484 |
| DOIs | |
| Publication status | Published - 2018 Feb 9 |
| Event | 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 - Singapore, Singapore Duration: 2017 Dec 10 → 2017 Dec 13 |
Other
| Other | 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 17/12/10 → 17/12/13 |
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Keywords
- electricity cost
- Genetic algorithm
- makespan cost
- time-of-use tariffs
- unrelated parallel machine scheduling with job delay mechanism
ASJC Scopus subject areas
- Business, Management and Accounting (miscellaneous)
- Industrial and Manufacturing Engineering
- Safety, Risk, Reliability and Quality
Cite this
A genetic algorithm for unrelated parallel machine scheduling minimizing makespan cost and electricity cost under time-of-use (TOU) tariffs with job delay mechanism. / Kurniawan, B.; Gozali, A. A.; Weng, W.; Fujimura, Shigeru.
2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017. Vol. 2017-December IEEE Computer Society, 2018. p. 583-587.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A genetic algorithm for unrelated parallel machine scheduling minimizing makespan cost and electricity cost under time-of-use (TOU) tariffs with job delay mechanism
AU - Kurniawan, B.
AU - Gozali, A. A.
AU - Weng, W.
AU - Fujimura, Shigeru
PY - 2018/2/9
Y1 - 2018/2/9
N2 - Unrelated parallel machine scheduling under time-of-use electricity price is addressed in this paper. In this setting, price of electricity can be different among various periods of the day. The objective is to minimize total cost consisting of makespan cost and electricity cost. Genetic algorithm (GA) is used to solve the unrelated parallel machine scheduling under time varying tariffs. Chromosome decoding, inspired by greedy total cost, is proposed to transform individual into feasible schedule. Furthermore, generated schedule from the individual is improved by job delay mechanism that shifts jobs to other periods to avoid high electricity cost. Finally, numerical experiment is conducted to implement the approach. Preliminary result shows that our proposed approach is effective and efficient to solve the corresponding problem.
AB - Unrelated parallel machine scheduling under time-of-use electricity price is addressed in this paper. In this setting, price of electricity can be different among various periods of the day. The objective is to minimize total cost consisting of makespan cost and electricity cost. Genetic algorithm (GA) is used to solve the unrelated parallel machine scheduling under time varying tariffs. Chromosome decoding, inspired by greedy total cost, is proposed to transform individual into feasible schedule. Furthermore, generated schedule from the individual is improved by job delay mechanism that shifts jobs to other periods to avoid high electricity cost. Finally, numerical experiment is conducted to implement the approach. Preliminary result shows that our proposed approach is effective and efficient to solve the corresponding problem.
KW - electricity cost
KW - Genetic algorithm
KW - makespan cost
KW - time-of-use tariffs
KW - unrelated parallel machine scheduling with job delay mechanism
UR - http://www.scopus.com/inward/record.url?scp=85045272386&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045272386&partnerID=8YFLogxK
U2 - 10.1109/IEEM.2017.8289958
DO - 10.1109/IEEM.2017.8289958
M3 - Conference contribution
VL - 2017-December
SP - 583
EP - 587
BT - 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
PB - IEEE Computer Society
ER -