Multi-objective evolutionary job-shop scheduling using jumping genes genetic algorithm

Kazi Shah Nawaz Ripon; Chi-Ho Tsang; Sam Kwong

doi:10.1109/IJCNN.2006.247291

Multi-objective evolutionary job-shop scheduling using jumping genes genetic algorithm

Kazi Shah Nawaz Ripon
, Chi-Ho Tsang
, Sam Kwong

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

16 Citations (Scopus)

Abstract

The Job-Shop Scheduling Problem (JSSP) is a hard combinatorial optimization problem. Several evolutionary approaches have been proposed to solve JSSP. But most of them are limited to single objective and fail in real-world applications, which naturally involve multiple objectives. In this paper, we present an evolutionary approach for solving multi-objective JSSP using Jumping Genes Genetic Algorithm (JGGA) that heuristically searches for the near-optimal solutions optimizing multiple criteria simultaneously. Experimental results reveal that our proposed approach can search for the near-optimal solutions by optimizing multiple criteria and also capable of finding a set of diverse and non-dominated scheduling solutions. © 2006 IEEE.

Original language	English
Title of host publication	The 2006 IEEE International Joint Conference on Neural Network Proceedings
Publisher	IEEE
Pages	3100-3107
ISBN (Print)	0780394909, 9780780394902
DOIs	https://doi.org/10.1109/IJCNN.2006.247291
Publication status	Published - 2006
Event	2006 International Joint Conference on Neural Networks (IJCNN '06) - Vancouver, BC, Canada Duration: 16 Jul 2006 → 21 Jul 2006

Publication series

Name
ISSN (Print)	2161-4393
ISSN (Electronic)	2161-4407

Conference

Conference	2006 International Joint Conference on Neural Networks (IJCNN '06)
Place	Canada
City	Vancouver, BC
Period	16/07/06 → 21/07/06

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10.1109/IJCNN.2006.247291

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@inproceedings{78a7b9ee1aaa444290ea50aa3802254b,

title = "Multi-objective evolutionary job-shop scheduling using jumping genes genetic algorithm",

abstract = "The Job-Shop Scheduling Problem (JSSP) is a hard combinatorial optimization problem. Several evolutionary approaches have been proposed to solve JSSP. But most of them are limited to single objective and fail in real-world applications, which naturally involve multiple objectives. In this paper, we present an evolutionary approach for solving multi-objective JSSP using Jumping Genes Genetic Algorithm (JGGA) that heuristically searches for the near-optimal solutions optimizing multiple criteria simultaneously. Experimental results reveal that our proposed approach can search for the near-optimal solutions by optimizing multiple criteria and also capable of finding a set of diverse and non-dominated scheduling solutions. {\textcopyright} 2006 IEEE.",

author = "Ripon, \{Kazi Shah Nawaz\} and Chi-Ho Tsang and Sam Kwong",

year = "2006",

doi = "10.1109/IJCNN.2006.247291",

language = "English",

isbn = "0780394909",

publisher = "IEEE",

pages = "3100--3107",

booktitle = "The 2006 IEEE International Joint Conference on Neural Network Proceedings",

address = "United States",

note = "2006 International Joint Conference on Neural Networks (IJCNN '06) ; Conference date: 16-07-2006 Through 21-07-2006",

}

Ripon, KSN, Tsang, C-H & Kwong, S 2006, Multi-objective evolutionary job-shop scheduling using jumping genes genetic algorithm. in The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, pp. 3100-3107, 2006 International Joint Conference on Neural Networks (IJCNN '06), Vancouver, BC, Canada, 16/07/06. https://doi.org/10.1109/IJCNN.2006.247291

Multi-objective evolutionary job-shop scheduling using jumping genes genetic algorithm. / Ripon, Kazi Shah Nawaz; Tsang, Chi-Ho; Kwong, Sam.
The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. p. 3100-3107.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Multi-objective evolutionary job-shop scheduling using jumping genes genetic algorithm

AU - Ripon, Kazi Shah Nawaz

AU - Tsang, Chi-Ho

AU - Kwong, Sam

PY - 2006

Y1 - 2006

N2 - The Job-Shop Scheduling Problem (JSSP) is a hard combinatorial optimization problem. Several evolutionary approaches have been proposed to solve JSSP. But most of them are limited to single objective and fail in real-world applications, which naturally involve multiple objectives. In this paper, we present an evolutionary approach for solving multi-objective JSSP using Jumping Genes Genetic Algorithm (JGGA) that heuristically searches for the near-optimal solutions optimizing multiple criteria simultaneously. Experimental results reveal that our proposed approach can search for the near-optimal solutions by optimizing multiple criteria and also capable of finding a set of diverse and non-dominated scheduling solutions. © 2006 IEEE.

AB - The Job-Shop Scheduling Problem (JSSP) is a hard combinatorial optimization problem. Several evolutionary approaches have been proposed to solve JSSP. But most of them are limited to single objective and fail in real-world applications, which naturally involve multiple objectives. In this paper, we present an evolutionary approach for solving multi-objective JSSP using Jumping Genes Genetic Algorithm (JGGA) that heuristically searches for the near-optimal solutions optimizing multiple criteria simultaneously. Experimental results reveal that our proposed approach can search for the near-optimal solutions by optimizing multiple criteria and also capable of finding a set of diverse and non-dominated scheduling solutions. © 2006 IEEE.

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U2 - 10.1109/IJCNN.2006.247291

DO - 10.1109/IJCNN.2006.247291

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 0780394909

SN - 9780780394902

SP - 3100

EP - 3107

BT - The 2006 IEEE International Joint Conference on Neural Network Proceedings

PB - IEEE

T2 - 2006 International Joint Conference on Neural Networks (IJCNN '06)

Y2 - 16 July 2006 through 21 July 2006

ER -

Multi-objective evolutionary job-shop scheduling using jumping genes genetic algorithm

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