A Hybrid cGA Applied to the MLCLSP with Overtime

Autor(es)

Toledo, Claudio F. M. ; Arantes, Marcio S. ; de Oliveira, Renato R. R. ; Delbem, Alexandre C. B.

• Tipo de documento: Artigo Científico
• Fonte: APPLIED COMPUTING REVIEW; v. 13, n. 3, p. 10-pg., 2013-09-01.

Resumo

A hybrid version of a compact genetic algorithm (cGA) is presented as approach to solve the Multi-Level Capacitated Lot Sizing Problem. The present paper extends results reported in [18]. The hybrid method combines a fix and optimize heuristic with cGA aiming to improve solutions generated by cGA. Also a linear mathematical programming model is solved to first evaluated solution provided by cGA. The performance of the hybrid compact genetic algorithm (HcGA) is evaluated over two sets of benchmark instances. The results are compared against methods from literature recently proposed for the same problem: two time-oriented decomposition heuristics and a hybrid multi-population genetic algorithm. A superior performance of HcGA is reported mainly for instances dealing with setup times and against time-oriented decomposition heuristics.(1) (AU)

• Processo FAPESP: 11/15534-5 - Heurísticas e metaheurísticas híbridas aplicadas ao problema capacitado de dimensionamento de lotes multi-nível
• Beneficiário: Claudio Fabiano Motta Toledo
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 11/15581-3 - Ambiente para desenvolvimento de métodos aplicados a problemas de otimização
• Beneficiário: Márcio da Silva Arantes
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 10/10133-0 - Problemas de corte, empacotamento, dimensionamento de lotes e programação da produção, e suas integrações em contextos industriais e logísticos
• Beneficiário: Reinaldo Morabito Neto
• Modalidade de apoio: Auxílio à Pesquisa - Temático