DocumentCode
617987
Title
Genetic algorithm, MIP and improvement heuristic applied to the MLCLP with backlogging
Author
Toledo, C.F.M. ; Hossomi, Marcelo Y. B. ; da Silva Arantes, Marcio ; Morelato Franca, Paulo
Author_Institution
Inst. of Math. & Comput. Sci., Univ. of Sao Paulo, Sao Carlos, Brazil
fYear
2013
fDate
20-23 June 2013
Firstpage
1483
Lastpage
1490
Abstract
The present paper solves the multi-level capacitated lot sizing problem with backlogging (MLCLSPB) combining a genetic algorithm with the solution of mixed-integer programming models and the improvement heuristic fix and optimize. This approach is evaluated over sets of benchmark instances and compared to methods from literature. Computational results indicate competitive results applying the proposed method when compared with other literature approaches.
Keywords
genetic algorithms; integer programming; lot sizing; MIP; MLCLP-with-backlogging; MLCLSPB; benchmark instances; genetic algorithm; heuristics; mixed-integer programming models; multilevel capacitated lot sizing problem-with-backlogging; Computational modeling; Genetic algorithms; Lot sizing; Mathematical model; Programming; Sociology; Statistics; genetic algorithm; hybrid metaheuristic; lot-sizing; multi-level;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation (CEC), 2013 IEEE Congress on
Conference_Location
Cancun
Print_ISBN
978-1-4799-0453-2
Electronic_ISBN
978-1-4799-0452-5
Type
conf
DOI
10.1109/CEC.2013.6557738
Filename
6557738
Link To Document