A Multi Objective Evolutionary Algorithm based on Decomposition for a Flow Shop Scheduling Problem in the Context of Industry 4.0

作     者：Rossit, Diego Gabriel Nesmachnow, Sergio Rossit, Daniel Alejandro 

作者机构：Univ Nacl Sur Dept Engn Bahia Blanca Buenos Aires Argentina Univ Nacl Sur INMABB Bahia Blanca Buenos Aires Argentina Consejo Nacl Invest Cient & Tecn Bahia Blanca Buenos Aires Argentina Univ Republica Montevideo Uruguay 

出 版 物：《INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES》 (Int. J. Math. Eng. Manag. Sci.)

年 卷 期：2022年第7卷第4期

页      面：433-454页

核心收录：

主　　题：Industry 4.0 Flow shop Missing operation Evolutionary algorithms Multi objective optimization Makespan Total tardiness 

摘      要：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.