In this paper, the flow-shop sequence-dependentgroup scheduling (FSDGS) problem is addressed with referenceto the makespan minimization objective. In order toeffectively cope with the issue at hand, a hybrid metaheuristicprocedure integrating features from genetic algorithmsand random sampling search methods has been developed.The proposed technique makes use of a matrix encoding ableto simultaneously manage the sequence of jobs within eachgroup and the sequence of groups to be processed alongthe flow-shop manufacturing system. A well-known problembenchmark arisen from literature, made by two, threeand six-machine instances has been taken as reference forboth tuning the relevant parameters of the proposed procedureand assessing performances of such approach againstthe two most recent algorithms presented in the body of literatureaddressing the FSDGS issue. The obtained results,also supported by a properly developed ANOVA analysis,demonstrate the superiority of the proposed hybrid metaheuristicin tackling the FSDGS problem under investigation.
|Titolo:||A hybrid genetic algorithm for minimizing makespan in a flow-shop sequence-dependent group scheduling problem|
|Data di pubblicazione:||2017|
|Citazione:||A hybrid genetic algorithm for minimizing makespan in a flow-shop sequence-dependent group scheduling problem / Costa, Antonio; Cappadonna, Fa; Fichera, Sergio. - In: JOURNAL OF INTELLIGENT MANUFACTURING. - ISSN 0956-5515. - 28:6(2017), pp. 1269-1283.|
|Appare nelle tipologie:||1.1 Articolo in rivista|