CORA - a heuristic approach to machine-part cell formation in the presence of alternative process plans

CORA - a heuristic approach to machine-part cell formation in the presence of alternative process... In this paper, a three-stage heuristic is proposed to solve the machine-part cell formation (MPCF) problem in which parts have alternative process plans, commonly known as the generalized group technology problem. In the first stage, the best process plan (part route) for each part is selected using the proposed route rank index (RRI), a ranking measure calculated from the correlation among the process plans (CoRa – Correlation based ranking). In the second stage, machine-part cells are identified with an objective to maximize the grouping efficacy. A fine-tuning module validates the covering set in the third stage. Computational performance of the proposed heuristic on a set of generalized group technology dataset available in the literature is presented. The process plans identified by CORA resulted in a higher grouping efficacy for 25% of the test instances and for the other test instances the grouping efficacy achieved was as good as the best results reported in literature. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

CORA - a heuristic approach to machine-part cell formation in the presence of alternative process plans

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer-Verlag London
Subject
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
ISSN
0268-3768
eISSN
1433-3015
D.O.I.
10.1007/s00170-017-0038-2
Publisher site
See Article on Publisher Site

Abstract

In this paper, a three-stage heuristic is proposed to solve the machine-part cell formation (MPCF) problem in which parts have alternative process plans, commonly known as the generalized group technology problem. In the first stage, the best process plan (part route) for each part is selected using the proposed route rank index (RRI), a ranking measure calculated from the correlation among the process plans (CoRa – Correlation based ranking). In the second stage, machine-part cells are identified with an objective to maximize the grouping efficacy. A fine-tuning module validates the covering set in the third stage. Computational performance of the proposed heuristic on a set of generalized group technology dataset available in the literature is presented. The process plans identified by CORA resulted in a higher grouping efficacy for 25% of the test instances and for the other test instances the grouping efficacy achieved was as good as the best results reported in literature.

Journal

The International Journal of Advanced Manufacturing TechnologySpringer Journals

Published: Feb 13, 2017

References

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