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1. Identity statement
Reference TypeJournal Article
Sitemtc-m16.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier6qtX3pFwXQZsFDuKxG/DbM9M
Repositorysid.inpe.br/marciana/2004/08.17.15.27   (restricted access)
Last Update2004:08.17.03.00.00 (UTC) administrator
Metadata Repositorysid.inpe.br/marciana/2004/08.17.15.27.49
Metadata Last Update2018:06.05.01.28.42 (UTC) administrator
Secondary KeyINPE-11221-PRE/6670
ISBN/ISSN0377-2217
ISSN0377-2217
Citation KeyNarcisoLore:1999:LaReGe
TitleLagrangean/surrogate relaxation for generalized assignment problems
Year1999
MonthApr.
Access Date2024, Apr. 28
Secondary TypePRE PI
Number of Files1
Size158 KiB
2. Context
Author1 Narciso, M. G.
2 Lorena, Luiz Antonio Nogueira
Resume Identifier1
2 8JMKD3MGP5W/3C9JHMQ
Group1 LAC-INPE-MCT-BR
JournalEuropean Journal of Operational Research
Volume114
Number1
Pages165-177
History (UTC)2006-09-28 22:36:24 :: administrator -> sergio ::
2008-01-07 12:49:58 :: sergio -> administrator ::
2018-06-05 01:28:42 :: administrator -> marciana :: 1999
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsrelaxation
Lagrangean and surrogate relaxation
generalized assignment problem / SUBGRADIENT METHOD
KNAPSACK-PROBLEM
SHORTEST-PATH
ALGORITHM
OPTIMIZATION
CONSTRAINTS
MODEL
AbstractThis work presents Lagrangean/surrogate relaxation to the problem of maximum profit assignment of n tasks to m agents (n > m), such that each task is assigned to only one agent subject to capacity constraints on the agents. The Lagrangean/surrogate relaxation combines usual Lagrangean and surrogate relaxations relaxing first a set of constraints in the surrogate way. Then, the Lagrangean relaxation of the surrogate constraint is obtained and approximately optimized (one-dimensional dual). The Lagrangean/surrogate is compared with the usual Lagrangean relaxation on a computational study using a large set of instances. The dual bounds are the same for both relaxations, but the Lagrangean/surrogate can give improved local bounds at the application of a subgradient method, resulting in less computational times. Three relaxations are derived for the problem. The first relaxation considers a vector of multipliers for the capacity constraints, the second for the assignment constraints and the other for the Lagrangean decomposition constraints. Relaxation multipliers are used with efficient constructive heuristics to find good feasible solutions. The application of a Lagrangean/surrogate approach seems very promising for large scale problems. (C) 1999 Elsevier Science B.V. All rights reserved.
AreaCOMP
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4. Conditions of access and use
Languageen
Target Filelagrangean.pdf
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sergio
Visibilityshown
Copy HolderSID/SCD
Archiving Policydenypublisher denyfinaldraft36
Read Permissiondeny from all and allow from 150.163
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ESGTTP
DisseminationWEBSCI; PORTALCAPES.
Host Collectionsid.inpe.br/banon/2003/08.15.17.40
6. Notes
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7. Description control
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