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

页码：433 / 454

页数：22

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