dc.contributor.author |
Raith, Andrea |
en |
dc.contributor.author |
Sedeño-Noda, A |
en |
dc.date.accessioned |
2017-06-15T03:21:25Z |
en |
dc.date.issued |
2017-06 |
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dc.identifier.citation |
Computers and Operations Research 82:153-166 Jun 2017 |
en |
dc.identifier.issn |
1873-765X |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/33538 |
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dc.description.abstract |
We address the problem of determining a complete set of extreme supported efficient solutions of biobjective minimum cost flow (BMCF) problems. A novel method improving the classical parametric method for this biobjective problem is proposed. The algorithm runs in O(Nn(m + nlogn)) time determining all extreme supported non-dominated points in the outcome space and one extreme supported efficient solution associated with each one of them. Here n is the number of nodes, m is the number of arcs and N is the number of extreme supported non-dominated points in outcome space for the BMCF problem. The memory space required by the algorithm is O(n + m) when the extreme supported efficient solutions are not required to be stored in RAM. Otherwise, the algorithm requires O(N + m) space. Extensive computational experiments comparing the performance of the proposed method and a standard parametric network simplex method are presented. |
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dc.publisher |
Pergamon Press Ltd. |
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dc.relation.ispartofseries |
Computers and Operations Research |
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dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from http://www.sherpa.ac.uk/romeo/issn/0305-0548/ |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.rights.uri |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
en |
dc.title |
Finding extreme supported solutions of biobjective network flow problems: An enhanced parametric programming approach |
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dc.type |
Journal Article |
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dc.identifier.doi |
10.1016/j.cor.2017.01.004 |
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pubs.begin-page |
153 |
en |
pubs.volume |
82 |
en |
dc.description.version |
AM - Accepted Manuscript |
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dc.rights.holder |
Copyright: Elsevier |
en |
pubs.end-page |
166 |
en |
pubs.publication-status |
Published |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Article |
en |
pubs.elements-id |
611671 |
en |
pubs.org-id |
Engineering |
en |
pubs.org-id |
Engineering Science |
en |
pubs.record-created-at-source-date |
2017-06-15 |
en |