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

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

14 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
Number of pages	8
ISBN (Print)	0780394909
DOIs	https://doi.org/10.1109/IJCNN.2006.247291
Publication status	Published - 2006
Externally published	Yes
Event	International Joint Conference on Neural Networks 2006 - Vancouver, Canada Duration: 16 Jul 2006 → 21 Jul 2006

Conference

Conference	International Joint Conference on Neural Networks 2006
Abbreviated title	IJCNN '06
Country/Territory	Canada
City	Vancouver
Period	16/07/06 → 21/07/06

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10.1109/IJCNN.2006.247291

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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",

pages = "3100--3107",

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

publisher = "IEEE",

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

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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, International Joint Conference on Neural Networks 2006, Vancouver, British Columbia, Canada, 16/07/06. https://doi.org/10.1109/IJCNN.2006.247291

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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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DO - 10.1109/IJCNN.2006.247291

M3 - Conference paper (refereed)

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EP - 3107

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

PB - IEEE

T2 - International Joint Conference on Neural Networks 2006

Y2 - 16 July 2006 through 21 July 2006

ER -

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

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