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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A hybrid multi-population genetic algorithm applied to solve the multi-level capacitated lot sizing problem with backlogging

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Author(s):
Motta Toledo, Claudio Fabiano [1] ; Ribeiro de Oliveira, Renato Resende [2] ; Franca, Paulo Morelato [3]
Total Authors: 3
Affiliation:
[1] Univ Sao Paulo, Inst Math & Comp Sci, BR-05508 Sao Paulo - Brazil
[2] Univ Fed Lavras, Dept Comp Sci, Lavras, MG - Brazil
[3] Univ Estadual Paulista, Dept Math & Comp, Sao Paulo - Brazil
Total Affiliations: 3
Document type: Journal article
Source: Computers & Operations Research; v. 40, n. 4, p. 910-919, APR 2013.
Web of Science Citations: 30
Abstract

The present paper proposes a new hybrid multi-population genetic algorithm (HMPGA) as an approach to solve the multi-level capacitated lot sizing problem with backlogging. This method combines a multi-population based metaheuristic using fix-and-optimize heuristic and mathematical programming techniques. A total of four test sets from the MULTILSB (Multi-Item Lot-Sizing with Backlogging) library are solved and the results are compared with those reached by two other methods recently published. The results have shown that HMPGA had a better performance for most of the test sets solved, specially when longer computing time is given. (C) 2012 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 10/10133-0 - Cutting, packing, lot-sizing and scheduling problems and their integration in industrial and logistics settings
Grantee:Reinaldo Morabito Neto
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 11/15581-3 - Environment for development of methods applied to optimization problems
Grantee:Márcio da Silva Arantes
Support Opportunities: Scholarships in Brazil - Master
FAPESP's process: 11/15534-5 - Hybrid heuristics and metaheuristics applied to the multi-level capacitated lot sizing problem
Grantee:Claudio Fabiano Motta Toledo
Support Opportunities: Regular Research Grants