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 ProceedingsConference paper (refereed)Researchpeer-review

14 Citations (Scopus)


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 languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
Publication statusPublished - 2006
Externally publishedYes


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