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Applications of Genetic Algorithm in Production Engineering-A Review


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1 Department of Production Engineering, Jadavpur University, Kolkata, India
     

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This article presents a literature review on the applications of genetic algorithm (GA) in the field of production engineering. Genetic algorithm is a multi-criteria decision-making tool that is used in almost all areas of production engineering. Out of many different applications of GA, this article covers a selective few that will be of wide Interest to the researchers and practitioners. This article critically analyzes some of the papers published in the international journals of high repute and gives a brief idea about many of the referred publications. Published papers are categorized on the basis of the areas of applications related to production engineering. The references are also grouped region-wise and year-wise in order to track the growth of GA applications. A total of 150 application papers are referred in this article. It Is hoped that this work will provide a ready reference on genetic algorithm and act as an informative summary kit for the researchers and practitioners for their future work.
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  • Applications of Genetic Algorithm in Production Engineering-A Review

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Authors

Shankar Chakraborty
Department of Production Engineering, Jadavpur University, Kolkata, India
Srijib Kumar Dhara
Department of Production Engineering, Jadavpur University, Kolkata, India

Abstract


This article presents a literature review on the applications of genetic algorithm (GA) in the field of production engineering. Genetic algorithm is a multi-criteria decision-making tool that is used in almost all areas of production engineering. Out of many different applications of GA, this article covers a selective few that will be of wide Interest to the researchers and practitioners. This article critically analyzes some of the papers published in the international journals of high repute and gives a brief idea about many of the referred publications. Published papers are categorized on the basis of the areas of applications related to production engineering. The references are also grouped region-wise and year-wise in order to track the growth of GA applications. A total of 150 application papers are referred in this article. It Is hoped that this work will provide a ready reference on genetic algorithm and act as an informative summary kit for the researchers and practitioners for their future work.