A genetic algorithm for facility layout

Suresh, Gamini; Vinod, V.V.; Sahu, Sumanta Kumar

doi:10.1080/00207549508904881

Cited by 45 publications

(19 citation statements)

References 15 publications

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“…Their results indicate that GA performs as well as stochastic quasi-gradient methods. There are also other studies in the literature in which the authors report successful applications of GAs to various manufacturing problems (an AGV network problem by McHaney (2000), assembly line optimization by Lee et al (2000), optimization of management parameters in flowlines by Fontanili et al (2000) and finding the best facility layout by Suresh et al (1995)). Stuckman et al (1991) compare GAs, simulated annealing and Bayesian/sampling algorithms, and conclude that GAs and simulated annealing are suitable for problems with high dimensionality.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Simulation optimization: A comprehensive review on theory and applications

Tekin¹,

Sabuncuoğlu

2004

IIE Transactions

212

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For several decades, simulation has been used as a descriptive tool by the operations research community in the modeling and analysis of a wide variety of complex real systems. With recent developments in simulation optimization and advances in computing technology, it now becomes feasible to use simulation as a prescriptive tool in decision support systems. In this paper, we present a comprehensive survey on techniques for simulation optimization with emphasis given on recent developments. We classify the existing techniques according to problem characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). We discuss the major advantages and possible drawbacks of the different techniques. A comprehensive bibliography and future research directions are also provided in the paper.

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Section: Evolutionary Algorithmsmentioning

confidence: 99%

Simulation optimization: A comprehensive review on theory and applications

Tekin¹,

Sabuncuoğlu

2004

IIE Transactions

212

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“…They could not Ficko, Palcic: Designing a Layout Using the Modified Triangle Method, and Genetic … implement an effective crossover as it disrupted the structural integrity of the parents. Suresh et al developed another GA to solve the QAP [24] and tried to eliminate some of the shortcomings of the GA proposed by Tate and Smith [23].…”

Section: Genetic Algorithms and The Facility Layout Problem -Literatumentioning

confidence: 99%

Designing a Layout Using the Modified Triangle Method, and Genetic Algorithms

Ficko¹,

Palčić²

2013

Int. j. simul. model.

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This paper describes the use of genetic algorithms (GA) for solving the facility layout problem (FLP) within manufacturing systems' design. The paper considers a specific heuristic layout planning method, known as the modified triangle method. This method seeks for an optimal layout solution based on the degrees of flows between workstations. The search for an optimal solution is extremely time-consuming and not suitable for larger systems; therefore we have developed a system based on evolutionary computation. Our paper presents a system, based on GA, and the results obtained regarding several numerical cases from the literature. We propose the usage of this system by presenting numerous advantages over other methods of solving FLP for problem representation and evolutionary computation for solution search.

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“…Mauridou and Pardolos (1997), have carried out a comprehensive survey on the use of genetic algorithms for facility layout. GAs have been successfully used in layout design (Suresh et al, 1995, Gupta et al, 1996, and have been shown to outperform human and KBS designs (Hamamoto, 1999). Parallel GAs were also used to solve the layout problem with geometric constraints (Tam and Chan, 1998).…”

Section: Eural Networkmentioning

confidence: 99%

Intelligent systems in manufacturing: current developments and future prospects

Meziane

Vadera

Kobbacy

et al. 2000

Integrated Manufacturing Systems

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Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence (AI) will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of AI techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different AI techniques to be considered and then shows how these AI techniques are used for the components of IMS.

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A genetic algorithm for facility layout

Cited by 45 publications

References 15 publications

Simulation optimization: A comprehensive review on theory and applications

Simulation optimization: A comprehensive review on theory and applications

Designing a Layout Using the Modified Triangle Method, and Genetic Algorithms

Intelligent systems in manufacturing: current developments and future prospects

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