Abstract

Cellular Manufacturing System (CMS) is an application of Group Technology (GT) that allows decomposing a manu-facturing system into subsystems. Grouping the machines and parts in a cellular manufacturing system, based on simi-larities is known as cell formation problem (CFP) which is an NP-hard problem. In this paper, a mathematical model is proposed for CFP and is solved using the Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Simulated Annealing (SA) meta-heuristic methods and the results are compared. The computational results show that the GA method is more effective in solving the model.

Highlights

  • Cellular Manufacturing System (CMS) is an application of the Group Technology (GT) philosophy that allows decomposing a manufacturing system into subsystems which makes its management easier than the entire manufacturing system

  • Grouping the machines and parts in a cellular manufacturing system, based on similarities is known as cell formation problem (CFP) which is an NP-hard problem

  • This paper discusses that the sequence of operations and the production volume are two major factors to be considered in the design of CMS

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Summary

Introduction

Cellular Manufacturing System (CMS) is an application of the Group Technology (GT) philosophy that allows decomposing a manufacturing system into subsystems which makes its management easier than the entire manufacturing system. Burbidge [4] defines group technology as: “an approach to the organization of work in which the organizational units are relatively independent groups, each responsible for the production of a given family of products” In this approach, the main goal is to form manufacturing groups in which, some machines are located in dedicated cells associated with some similar parts based on a machine-part incidence matrix. 3) In a large number of researches, the total number of “ones” fell out of diagonal blocks is considered as a measure of the number of intercellular movements between the cells This value is seriously dictated by the sequence through which parts are processed.

Notations
Mathematical Formulation
The ACO Algorithm
Solution Representation and Evaluation
Goodness Measurement
Initialize
Until stopping condition is met
Genetic Algorithm
The GA Algorithm
Simulated Annealing
The SA Algorithm
Examples
Conclusions
Full Text
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