Home > Research > Publications & Outputs > Solving the distributed two machine flow-shop s...

Research

Associated organisational unit

Electronic data

  • solving-distributed-two-1

    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-61824-1_49

    Accepted author manuscript, 240 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Solving the distributed two machine flow-shop scheduling problem using differential evolution

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

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 -