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A simulated annealing for the cell formation problem with ratio level data

A simulated annealing for the cell formation problem with ratio level data In this paper, the cell formation problem is considered with ratio level data with an objective of minimising the cell load variation. The attempt has been made to propose a simulated annealing (SA) based on the perturbation scheme as random insertion perturbation scheme (RIPS). The ratio level data is distinguished by utilising the workload information gathered from process times, production quantity of parts and also from the capacity of the machines. A modified grouping efficiency (MGE) is used to measure the performance of the system. From the results it is observed that the simulated annealing produces the solution does not differ significantly from the optimal solutions for the benchmark problems. The algorithms which we have chosen the benchmark problems are K-means, modified ART1 and genetic algorithm taken from the literature. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Enterprise Network Management Inderscience Publishers

A simulated annealing for the cell formation problem with ratio level data

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1748-1252
eISSN
1748-1260
DOI
10.1504/IJENM.2019.098107
Publisher site
See Article on Publisher Site

Abstract

In this paper, the cell formation problem is considered with ratio level data with an objective of minimising the cell load variation. The attempt has been made to propose a simulated annealing (SA) based on the perturbation scheme as random insertion perturbation scheme (RIPS). The ratio level data is distinguished by utilising the workload information gathered from process times, production quantity of parts and also from the capacity of the machines. A modified grouping efficiency (MGE) is used to measure the performance of the system. From the results it is observed that the simulated annealing produces the solution does not differ significantly from the optimal solutions for the benchmark problems. The algorithms which we have chosen the benchmark problems are K-means, modified ART1 and genetic algorithm taken from the literature.

Journal

International Journal of Enterprise Network ManagementInderscience Publishers

Published: Jan 1, 2019

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