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

共 79 条

[71] Optimal design of vehicle components using topology design and optimisation
Yildiz, AR
Kaya, N
Öztürk, F
[J]. INTERNATIONAL JOURNAL OF VEHICLE DESIGN, 2004, 34 (04) : 387 - 398
[72] Integrated optimal topology design and shape optimization using neural networks
Yildiz, AR
Öztürk, N
Kaya, N
Öztürk, F
[J]. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2003, 25 (04) : 251 - 260
[73] Structural design of vehicle components using gravitational search and charged system search algorithms
Yildiz, Betul Sultan
Lekesiz, Huseyin
Yildiz, Ali Riza
[J]. MATERIALS TESTING, 2016, 58 (01) : 79 - 81
[74] Yldz AR, 2008, MATER TEST, V50, P224
[75] An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem
Zhang, Guohui
Shao, Xinyu
Li, Peigen
Gao, Liang
[J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2009, 56 (04) : 1309 - 1318
[76] Robust scheduling for multi-objective flexible job-shop problems with flexible workdays
Zhang, Jiae
Yang, Jianjun
Zhou, Yong
[J]. ENGINEERING OPTIMIZATION, 2016, 48 (11) : 1973 - 1989
[77] Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model
Zhang, Qingfu
Liu, Wudong
Tsang, Edward
Virginas, Botond
[J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2010, 14 (03) : 456 - 474
[78] Performance assessment of multiobjective optimizers: An analysis and review
Zitzler, E
Thiele, L
Laumanns, M
Fonseca, CM
da Fonseca, VG
[J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2003, 7 (02) : 117 - 132
[79] The bootstrap and its application in signal processing
Zoubir, AM
Boashash, B
[J]. IEEE SIGNAL PROCESSING MAGAZINE, 1998, 15 (01) : 56 - 76

← 1 2 3 4 5 6 7 8 →