Manufacturing facilities layout design using Genetic Algorithm

Balamurugan, K.; Selladurai, V.; Ilamathi, B

doi:10.1504/ijmtm.2008.017739

Cited by 4 publications

(1 citation statement)

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“…Lee et al [23] proposed an improved GA to derive solutions for multi-floor facility layout problems. Balamurugan et al [24] used GA to solve a multi-objective machine layout problem with unequal area and fixed shape.…”

Section: Literature Surveymentioning

confidence: 99%

An artificial bee colony algorithm for design and optimize the fixed area layout problems

Saravanan

Arulkumar²

2015

Int J Adv Manuf Technol

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The placement of production equipments plays a major role in designing a layout in cellular manufacturing. The better placement increases the productivity. This article introduces a new algorithm to design and optimize a fixed area cellular layout problem by using an artificial bee colony (ABC) technique which is based on the intelligent foraging behavior of a honeybee. The objective of this article is to determine the physical arrangement of work centers by minimizing the total traveling distance of the product. Volume of the product and distance between the work centers are the important factors that affect layout design. Some relative importance factors like priority of products, hazardous moves, and back-tracking moves are considered in this article. Layout moment ratio helps to compare the different proposed layouts. The higher layout moment ratio is the more desirable layout. Also this article compares the results of ABC technique with genetic algorithm (GA) and simulated annealing (SA) algorithm based on the total moment value, layout moment ratio, number of iterations, computation time, and back-tracking movements. Finally, it concluded that ABC is a better technique to solve fixed area cellular layout problems than the mentioned algorithms with high dimensionality.

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Section: Literature Surveymentioning

confidence: 99%

An artificial bee colony algorithm for design and optimize the fixed area layout problems

Saravanan

Arulkumar²

2015

Int J Adv Manuf Technol

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Pareto Optimal Based Evolutionary Approach for Solving Multi-Objective Facility Layout Problem

Ripon

Glette

Mirmotahari

et al. 2009

Neural Information Processing

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A simple heuristic for linear sequencing of machines in layout design

Kumar¹,

Islam

Lenin

et al. 2011

International Journal of Production Research

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This paper presents a simple heuristic to determine a common linear machine sequence for multiple products with different operation sequences and a limited number of duplicate machine types available for the job. The heuristic is based on minimization of the total flow distance traveled by a product on the linear machine sequence. It is assumed that the flows of products are allowed only in the forward direction, either in-sequence or bypass. It is also assumed that backtrack movements are not allowed. The effectiveness of the proposed heuristic is demonstrated through the solutions of two typical layout design problems taken from the literature. Subsequently, a number of additional problems are solved and their results are compared with the results applying existing methods. The results indicate that the proposed method can be an effective tool in solving layout design problems.

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Manufacturing facilities layout design using Genetic Algorithm

Cited by 4 publications

References 0 publications

An artificial bee colony algorithm for design and optimize the fixed area layout problems

An artificial bee colony algorithm for design and optimize the fixed area layout problems

Pareto Optimal Based Evolutionary Approach for Solving Multi-Objective Facility Layout Problem

A simple heuristic for linear sequencing of machines in layout design

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