Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

被引：19

作者：

Tan, Choo Jun ^{[1
]}

Neoh, Siew Chin ^{[2
]}

Lim, Chee Peng ^{[3
]}

Hanoun, Samer ^{[3
]}

Wong, Wai Peng ^{[4
]}

Loo, Chu Kong ^{[5
]}

Zhang, Li ^{[6
]}

Nahavandi, Saeid ^{[3
]}

机构：

[1] Wawasan Open Univ, Sch Sci & Technol, George Town, Malaysia

[2] UCSI Univ, Fac Engn Technol & Built Environm, Kuala Lumpur, Malaysia

[3] Deakin Univ, Inst Intelligent Syst Res & Innovat, Geelong, Vic, Australia

[4] Univ Sci Malaysia, Sch Management, George Town, Malaysia

[5] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Artificial Intelligence, Kuala Lumpur, Malaysia

[6] Northumbria Univ, Dept Comp Sci & Digital Technol, Fac Engn & Environm, Newcastle Upon Tyne, Tyne & Wear, England

来源：

JOURNAL OF INTELLIGENT MANUFACTURING | 2019年 / 30卷 / 02期

关键词：

Multi-objective optimisation; Evolutionary algorithm; Ensemble model; Job-shop scheduling; MULTIOBJECTIVE GENETIC ALGORITHM; OPTIMIZATION; FLOWSHOP; FRAMEWORK; PARAMETERS; OPTIMALITY; MULTIPLE; SUPPORT; SEARCH; DESIGN;

D O I：

10.1007/s10845-016-1291-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a novel evolutionary algorithm is applied to tackle job-shop scheduling tasks in manufacturing environments. Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation problems in job-shop scheduling. The MmGA ensemble is able to approximate the optimal solution under the Pareto optimality principle. To evaluate the effectiveness of the MmGA ensemble, a case study based on real requirements is conducted. The results positively indicate the effectiveness of the MmGA ensemble in undertaking job-shop scheduling problems.

引用

页码：879 / 890

页数：12

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