Diego Gabriel Rossit
Department of Engineering and INMABB, Universidad Nacional del Sur and CONICET, Bahía Blanca, Buenos Aires, Argentina.

Sergio Nesmachnow
Universidad de la República, Uruguay.

Daniel Alejandro Rossit
Department of Engineering and INMABB, Universidad Nacional del Sur and CONICET, Bahía Blanca, Buenos Aires, Argentina.

DOI https://doi.org/10.33889/IJMEMS.2022.7.4.029

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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 multi objective evolutionary approach based on decomposition for efficiently addressing the multi objective 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.

Keywords- Industry 4.0, Flow shop, Missing operation, Evolutionary algorithms, Multi objective optimization, Makespan, Total tardiness.

Citation

Rossit, D. G., Nesmachnow, S., & Rossit, D. A. (2022). A Multi Objective Evolutionary Algorithm based on Decomposition for a Flow Shop Scheduling Problem in the Context of Industry 4.0. International Journal of Mathematical, Engineering and Management Sciences, 7(4), 433-454. https://doi.org/10.33889/IJMEMS.2022.7.4.029.