Standard
Solving the distributed two machine flow-shop scheduling problem using differential evolution. /
Dempster, P.; Li, P.; Drake, J.H.
Advances in Swarm Intelligence: 8th International Conference, ICSI 2017, Fukuoka, Japan, July 27 – August 1, 2017, Proceedings, Part I. ed. / Ying Tan; Hideyuki Tagaki; Yuhui Shi. Cham: Springer, 2017. p. 449-457 (Lecture Notes in Computer Science; Vol. 10385).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Dempster, P, Li, P & Drake, JH 2017,
Solving the distributed two machine flow-shop scheduling problem using differential evolution. in Y Tan, H Tagaki & Y Shi (eds),
Advances in Swarm Intelligence: 8th International Conference, ICSI 2017, Fukuoka, Japan, July 27 – August 1, 2017, Proceedings, Part I. Lecture Notes in Computer Science, vol. 10385, Springer, Cham, pp. 449-457, The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-61824-1_49, Fukuoka, Japan,
28/07/17.
https://doi.org/10.1007/978-3-319-61824-1_49
APA
Dempster, P., Li, P., & Drake, J. H. (2017).
Solving the distributed two machine flow-shop scheduling problem using differential evolution. In Y. Tan, H. Tagaki, & Y. Shi (Eds.),
Advances in Swarm Intelligence: 8th International Conference, ICSI 2017, Fukuoka, Japan, July 27 – August 1, 2017, Proceedings, Part I (pp. 449-457). (Lecture Notes in Computer Science; Vol. 10385). Springer.
https://doi.org/10.1007/978-3-319-61824-1_49
Vancouver
Dempster P, Li P, Drake JH.
Solving the distributed two machine flow-shop scheduling problem using differential evolution. In Tan Y, Tagaki H, Shi Y, editors, Advances in Swarm Intelligence: 8th International Conference, ICSI 2017, Fukuoka, Japan, July 27 – August 1, 2017, Proceedings, Part I. Cham: Springer. 2017. p. 449-457. (Lecture Notes in Computer Science). Epub 2017 Jun 24. doi: 10.1007/978-3-319-61824-1_49
Author
Dempster, P. ; Li, P. ; Drake, J.H. /
Solving the distributed two machine flow-shop scheduling problem using differential evolution. Advances in Swarm Intelligence: 8th International Conference, ICSI 2017, Fukuoka, Japan, July 27 – August 1, 2017, Proceedings, Part I. editor / Ying Tan ; Hideyuki Tagaki ; Yuhui Shi. Cham : Springer, 2017. pp. 449-457 (Lecture Notes in Computer Science).
Bibtex
@inproceedings{dcad992390d2450a9eb613a06f8bae62,
title = "Solving the distributed two machine flow-shop scheduling problem using differential evolution",
abstract = "Flow-shop scheduling covers a class of widely studied optimisation problem which focus on optimally sequencing a set of jobs to be processed on a set of machines according to a given set of constraints. Recently, greater research attention has been given to distributed variants of this problem. Here we concentrate on the distributed two machine flow-shop scheduling problem (DTMFSP), a special case of classic two machine flow-shop scheduling, with the overall goal of minimising makespan. We apply Differential Evolution to solve the DTMFSP, presenting new best-known results for some benchmark instances from the literature. A comparison to previous approaches from the literature based on the Harmony Search algorithm is also given.",
author = "P. Dempster and P. Li and J.H. Drake",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-61824-1_49; The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-61824-1_49 ; Conference date: 28-07-2017 Through 01-08-2017",
year = "2017",
month = aug,
day = "1",
doi = "10.1007/978-3-319-61824-1_49",
language = "English",
isbn = "9783319618234",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "449--457",
editor = "Tan, {Ying } and Hideyuki Tagaki and Yuhui Shi",
booktitle = "Advances in Swarm Intelligence",
url = "https://searchworks.stanford.edu/view/14007836",
}
RIS
TY - GEN
T1 - Solving the distributed two machine flow-shop scheduling problem using differential evolution
AU - Dempster, P.
AU - Li, P.
AU - Drake, J.H.
N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-61824-1_49
PY - 2017/8/1
Y1 - 2017/8/1
N2 - Flow-shop scheduling covers a class of widely studied optimisation problem which focus on optimally sequencing a set of jobs to be processed on a set of machines according to a given set of constraints. Recently, greater research attention has been given to distributed variants of this problem. Here we concentrate on the distributed two machine flow-shop scheduling problem (DTMFSP), a special case of classic two machine flow-shop scheduling, with the overall goal of minimising makespan. We apply Differential Evolution to solve the DTMFSP, presenting new best-known results for some benchmark instances from the literature. A comparison to previous approaches from the literature based on the Harmony Search algorithm is also given.
AB - Flow-shop scheduling covers a class of widely studied optimisation problem which focus on optimally sequencing a set of jobs to be processed on a set of machines according to a given set of constraints. Recently, greater research attention has been given to distributed variants of this problem. Here we concentrate on the distributed two machine flow-shop scheduling problem (DTMFSP), a special case of classic two machine flow-shop scheduling, with the overall goal of minimising makespan. We apply Differential Evolution to solve the DTMFSP, presenting new best-known results for some benchmark instances from the literature. A comparison to previous approaches from the literature based on the Harmony Search algorithm is also given.
U2 - 10.1007/978-3-319-61824-1_49
DO - 10.1007/978-3-319-61824-1_49
M3 - Conference contribution/Paper
SN - 9783319618234
T3 - Lecture Notes in Computer Science
SP - 449
EP - 457
BT - Advances in Swarm Intelligence
A2 - Tan, Ying
A2 - Tagaki, Hideyuki
A2 - Shi, Yuhui
PB - Springer
CY - Cham
T2 - The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-61824-1_49
Y2 - 28 July 2017 through 1 August 2017
ER -
