Finding extreme supported solutions of biobjective network flow problems: An enhanced parametric programming approach

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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 en
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 en
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. en
dc.publisher Pergamon Press Ltd. en
dc.relation.ispartofseries Computers and Operations Research en
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 en
dc.type Journal Article en
dc.identifier.doi 10.1016/j.cor.2017.01.004 en
pubs.begin-page 153 en
pubs.volume 82 en
dc.description.version AM - Accepted Manuscript en
dc.rights.holder Copyright: Elsevier en
pubs.end-page 166 en
pubs.publication-status Published en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Article en
pubs.elements-id 611671 en
pubs.org-id Faculty of Engineering en
pubs.org-id Engineering Science en
pubs.record-created-at-source-date 2017-06-15 en


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http://creativecommons.org/licenses/by-nc-nd/4.0/ Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/

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