Machine part cell formation is the group technology problem, in which the parts with near similar machining requirements are grouped into part families and the corresponding machines into machine cells. In this paper, a genetic algorithm with a fine tuning procedure is proposed to solve the group technology problem considering only one process plan for each part. The grouping efficacy achieved by the proposed method is comparable to the existing methods in general and better for 11.42% of the datasets. © Springer International Publishing AG 2018.

Valarmathi B

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Srinivasa Gupta N

Department of Manufacturing Engineering

School of Mechanical Engineering

Sowmiya N

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Solving Machine Part Cell Formation Problem Using Genetic Algorithm Based Evolutionary Computing

Advances in Intelligent Systems and Computing Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016)

Machine-part cell formation is the process of identifying part families and the appropriate machine cell for each part family. Grouping efficacy (GE), the widely used measure for assessing the goodness of the machine-part cells depends on identification of correct part families and the appropriate machine cell for each part family. In this paper, a heuristic based on correlation analysis and relevance index is proposed for the formation of machine-part cells. Computational performance of the proposed heuristic on a set of group technology data-set available in the literature is also presented. GE of the solutions produced by the proposed heuristic is equal to the best efficacy reported in the literature for 63% of the test instances and improved the GE for 6% of the total test instances. © 2014 Taylor &amp; Francis.

International Journal of Production Research

CARI – a heuristic approach to machine-part cell formation using correlation analysis and relevance index

Cellular manufacturing system design using grouping efficacy-based genetic algorithm

Expert Systems with Applications

Genetic algorithm approach for solving a cell formation problem in cellular manufacturing

An Enhanced Grouping Genetic Algorithm for solving the cell formation problem

The International Journal of Advanced Manufacturing Technology

Genetic cell formation using ratio level data in cellular manufacturing systems

Journal	Data powered by TypesetAdvances in Intelligent Systems and Computing Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016)
Publisher	Data powered by TypesetSpringer International Publishing
ISSN	2194-5357
Open Access	No