Particle swarm optimization for bi-level pricing problems in supply chains

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dc.contributor.author Gao, Y
dc.contributor.author Zhang, G
dc.contributor.author Lu, J
dc.contributor.author Wee, H
dc.date.accessioned 2012-10-12T03:32:48Z
dc.date.issued 2011-01
dc.identifier.citation Journal Of Global Optimization, 2011, 51 (2), pp. 245 - 254
dc.identifier.issn 0925-5001
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/17944
dc.description.abstract With rapid technological innovation and strong competition in hi-tech industries such as computer and communication organizations, the upstream component price and the downstream product cost usually decline significantly with time. As a result, an effective pricing supply chain model is very important. This paper first establishes two bi-level pricing models for pricing problems with the buyer and the vendor in a supply chain designated as the leader and the follower, respectively. A particle swarm optimization (PSO) based algorithm is developed to solve problems defined by these bi-level pricing models. Experiments illustrate that this PSO based algorithm can achieve a profit increase for buyers or vendors if they are treated as the leaders under some situations, compared with the existing methods.
dc.language English
dc.publisher Springer New York LLC
dc.relation.hasversion Accepted manuscript version
dc.relation.isbasedon 10.1007/s10898-010-9595-8
dc.title Particle swarm optimization for bi-level pricing problems in supply chains
dc.type Journal Article
dc.parent Journal Of Global Optimization
dc.journal.volume 2
dc.journal.volume 51
dc.journal.number 2 en_US
dc.publocation United States en_US
dc.identifier.startpage 245 en_US
dc.identifier.endpage 254 en_US
dc.cauo.name FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.for 0802 Computation Theory and Mathematics
dc.for 0103 Numerical and Computational Mathematics
dc.for 0102 Applied Mathematics
dc.personcode 001038
dc.personcode 020014
dc.personcode 102497
dc.percentage 34 en_US
dc.classification.name Applied Mathematics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Two-stage supply chain, Bi-level programming, Hierarchical decision-making, Optimization, Particle swarm optimization en_US
dc.description.keywords Wind speed multi-step forecasting
dc.description.keywords Empirical mode decomposition
dc.description.keywords Feed-forward neural network
dc.description.keywords High frequency
dc.description.keywords Partial autocorrelation function
dc.description.keywords Two-stage supply chain, Bi-level programming, Hierarchical decision-making, Optimization, Particle swarm optimization
dc.description.keywords Two-stage supply chain, Bi-level programming, Hierarchical decision-making, Optimization, Particle swarm optimization
dc.description.keywords Two-stage supply chain, Bi-level programming, Hierarchical decision-making, Optimization, Particle swarm optimization
dc.description.keywords Two-stage supply chain, Bi-level programming, Hierarchical decision-making, Optimization, Particle swarm optimization
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Open Access
utslib.copyright.date 2015-04-15 12:23:47.074767+10


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