A hybrid genetic algorithm for minimizing makespan in a flow-shop sequence-dependent group scheduling problem

In this paper, the flow-shop sequence-dependent group scheduling (fsdgs) problem is addressed with reference to the makespan minimization objective. in order to effectively cope with the issue at hand, a hybrid metaheuristic procedure integrating features from genetic algorithms and random sampling search methods has been developed. the proposed technique makes use of a matrix encoding able to simultaneously manage the sequence of jobs within each group and the sequence of groups to be processed along the flow-shop manufacturing system. a well-known problem benchmark arisen from literature, made by two, three and six-machine instances has been taken as reference for both tuning the relevant parameters of the proposed procedure and assessing performances of such approach against the two most recent algorithms presented in the body of literature addressing the fsdgs issue. the obtained results, also supported by a properly developed anova analysis, demonstrate the superiority of the proposed hybrid metaheuristic in tackling the fsdgs problem under investigation.