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

被引：7
|
作者：
Rossit, Diego Gabriel [1 ,2 ,3 ]
Nesmachnow, Sergio [4 ]
Rossit, Daniel Alejandro [1 ,2 ,3 ]
机构：
[1] Univ Nacl Sur, Dept Engn, Bahia Blanca, Buenos Aires, Argentina
[2] Univ Nacl Sur, INMABB, Bahia Blanca, Buenos Aires, Argentina
[3] Consejo Nacl Invest Cient & Tecn, Bahia Blanca, Buenos Aires, Argentina
[4] Univ Republica, Montevideo, Uruguay
来源：
INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES | 2022年 / 7卷 / 04期
关键词：
Industry; 4.0; Flow shop; Missing operation; Evolutionary algorithms; Multi objective optimization; Makespan; Total tardiness; NON-PERMUTATION SCHEDULES; MANUFACTURING SYSTEMS; PERFORMANCE; MOEA/D; RULES;
D O I：
10.33889/IJMEMS.2022.7.4.029
中图分类号：
T [工业技术];
学科分类号：
08 ;
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.
引用
收藏
页码：433 / 454
页数：22
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