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  • 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

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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
Publication date1/08/2017
Host publicationAdvances in Swarm Intelligence: 8th International Conference, ICSI 2017, Fukuoka, Japan, July 27 – August 1, 2017, Proceedings, Part I
EditorsYing Tan, Hideyuki Tagaki, Yuhui Shi
Place of PublicationCham
PublisherSpringer
Pages449-457
Number of pages9
ISBN (electronic)9783319618241
ISBN (print)9783319618234
<mark>Original language</mark>English
EventThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-61824-1_49 - Fukuoka, Japan
Duration: 28/07/2017 → 1/08/2017
https://searchworks.stanford.edu/view/14007836

Conference

ConferenceThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-61824-1_49
Country/TerritoryJapan
CityFukuoka
Period28/07/17 → 1/08/17
Internet address

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10385
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Conference

ConferenceThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-61824-1_49
Country/TerritoryJapan
CityFukuoka
Period28/07/17 → 1/08/17
Internet address

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.

Bibliographic note

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