Greedy-Based Non-Dominated Sorting Genetic Algorithm III for Optimizing Single-Machine Scheduling Problem With Interfering Jobs

被引：5

作者：

Cheng, Chen-Yang ^{[1
]}

Lin, Shih-Wei ^{[2
,3
,4
]}

Pourhejazy, Pourya ^{[1
]}

Ying, Kuo-Ching ^{[1
]}

Li, Shu-Fen ^{[5
]}

Liu, Ying-Chun ^{[6
]}

机构：

[1] Natl Taipei Univ Technol, Dept Ind Engn & Management, Taipei 10608, Taiwan

[2] Chang Gung Univ, Dept Informat Management, Taoyuan 33302, Taiwan

[3] Linkou Chang Gung Mem Hosp, Dept Neurol, Taoyuan 33305, Taiwan

[4] Ming Chi Univ Technol, Dept Ind Engn & Management, New Taipei 24301, Taiwan

[5] Natl Chin Yi Univ Technol, Dept Ind Engn & Management, Taichung 41170, Taiwan

[6] Minist Sci & Technol Taiwan, Hsinchu Sci Pk Burau, Hsinchu 30016, Taiwan

来源：

IEEE ACCESS | 2020年 / 8卷

关键词：

Job shop scheduling; Optimization; Single machine scheduling; Sorting; Genetic algorithms; Scheduling; interfering jobs; multi-objective optimization; non-dominated solutions; metaheuristics; OPTIMIZATION;

D O I：

10.1109/ACCESS.2020.3014134

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Given the importance of production planning and control in the design of flexible services and manufacturing systems, scheduling problems with interfering jobs are much-needed optimization tools to respond to heterogeneous and fluctuating market demands in a timely fashion. This study contributes to the scheduling literature developing an effective multi-objective (M-O) metaheuristic to solve the Single-machine Scheduling Problems with Interfering Jobs (SSP-IJs). Integrating a local search-based mechanism into the evolutionary search procedure, a Greedy-based non-dominated sorting genetic algorithm III (GNSGA-III) is proposed that effectively explores multi-objective solution environments. Various performance indicators within extensive numerical tests are used to compare the performance of the GNSGA-III with that of the best-performing benchmark algorithm in the literature developed to solve the SSP-IJs. Statistical tests verify that the developed multi-objective optimization algorithm is superior with respect to various performance indicators. Applications of the developed solution approach are worthwhile topics to help advance multi-objective optimization problems.

引用

页码：142543 / 142556

页数：14

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