A Multiobjective Evolutionary Algorithm based on Decomposition for a flow shop scheduling problem in the context of Industry 4.0

Abstract

Under the novel paradigm of Industry 4.0, missing operations have arisen as a result of the increasingly customization of the industrial products in which customers have an extended control over the characteristics of the final products. As a result, this has completely modified the scheduling and planning management of jobs in modern factories. As a contribution in this area, this article presents a multiobjective evolutionary approach based on decomposition for efficiently addressing the multiobjective flow shop problem with missing operations, a relevant problem in modern industry. Tests performed over a representative set of instances show the competitiveness of the proposed approach when compared with other baseline metaheuristics.