Manufacturing cell design: an integer programming model employing genetic algorithms

Joines, Jeffrey A.; Culbreth, C. Thomas; King, Robert E.

doi:10.1080/07408179608966253

Cited by 115 publications

(72 citation statements)

References 46 publications

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“…Earlier, similar views were given by Mungawattana [49]. A large number of researchers used GA to solve the CFP such as Venugopal and Narendran [61], Joines et al [62], Gravel et al [57], Mak and Wong [63], Zhao and Wu [58], Wu et al [64], Chu and Tsai [10], and Chan et al [65].…”

Section: Introduction and Literature Reviewmentioning

confidence: 86%

A Comparison of a Standard Genetic Algorithm with a Hybrid Genetic Algorithm Applied to Cell Formation Problem

Javaid

Tariq

Hussain

2014

Advances in Mechanical Engineering

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Though there are a number of benefits associated with cellular manufacturing systems, its implementation (identification of part families and corresponding machine groups) for real life problems is still a challenging task. To handle the complexity of optimizing multiple objectives and larger size of the problem, most of the researchers in the past two decades or so have focused on developing genetic algorithm (GA) based techniques. Recently this trend has shifted from standard GA to hybrid GA (HGA) based approaches in the quest for greater effectiveness as far as convergence on to the optimum solution is concerned. In order to prove the point, that HGAs possess better convergence abilities than standard GAs, a methodology, initially based on standard GA and later on hybridized with a local search heuristic (LSH), has been developed during this research. Computational experience shows that HGA maintains its accuracy level with increase in problem size, whereas standard GA looses its effectiveness as the problem size grows.

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Section: Introduction and Literature Reviewmentioning

confidence: 86%

A Comparison of a Standard Genetic Algorithm with a Hybrid Genetic Algorithm Applied to Cell Formation Problem

Javaid

Tariq

Hussain

2014

Advances in Mechanical Engineering

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“…Unlike linear or local optimizations, GAs seek the global optimum on complex surfaces, theoretically without restriction, but with no guarantee of reaching it (Haupt and Haupt 2004). The objective function to be optimized (often named the fitness function within this context) can be of various types (Joines et al 1996), but GAs must be adapted in order to perform optimally.…”

Section: Assessed Genetic Algorithm Variantsmentioning

confidence: 99%

Global Optimization of an Analog Method by Means of Genetic Algorithms

Horton

Jaboyedoff

Obled

2017

Monthly Weather Review

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Analog methods are based on a statistical relationship between synoptic meteorological variables (predictors) and local weather (predictand, to be predicted). This relationship is defined by several parameters, which are often calibrated by means of a semiautomatic sequential procedure. This calibration approach is fast, but has strong limitations. It proceeds through successive steps, and thus cannot handle all parameter dependencies. Furthermore, it cannot automatically optimize some parameters, such as the selection of pressure levels and temporal windows (hours of the day) at which the predictors are compared. To overcome these limitations, the global optimization technique of genetic algorithms is considered, which can jointly optimize all parameters of the method, and get closer to a global optimum, by taking into account the dependencies of the parameters. Moreover, it can objectively calibrate parameters that were previously assessed manually and can take into account new degrees of freedom. However, genetic algorithms must be tailored to the problem under consideration. Multiple combinations of algorithms were assessed, and new algorithms were developed (e.g., the chromosome of adaptive search radius, which is found to be very robust), in order to provide recommendations regarding the use of genetic algorithms for optimizing several variants of analog methods. A global optimization approach provides new perspectives for the improvement of analog methods, and for their application to new regions or new predictands.

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“…Por exemplo, se o parâmetro q for igual a 1, somente o melhor indivíduo terá valor de probabilidade diferente de zero e, portanto, somente ele será escolhido. Nos testes preliminares utilizamos no AE proposto, diferentes valores para q (0.6, 0.7, 0.8, 0.9 e 0.1) e para q = 0.7 foi obtido os melhores resultados, valor este que foi então usado para o conjunto das instâncias da literatura (em [JCK96] foi usado o valor q = 0.10).…”

Section: Estratégia De Seleção Dos Indivíduos Reprodutoresunclassified

“…Os resultados médios não estão listados, mas o maior gap entre a pior solução (das 10 execuções) e a melhor nunca ultrapassou 3%. Adicionalmente o AE foi comparado com o algoritmo genético proposto por Joines et al em [JCK96], e aqui denominado AG-JCK (este algoritmo da literatura não disponibiliza nem instâncias nem códigos, desta forma este algoritmo foi implementado novamente neste trabalho).…”

Section: N 1intrak = Número De Elementos Do Cluster K Iguais a 1 Se Aunclassified

Um algoritmo evolutivo híbrido para a formação de células de manufatura em sistemas de produção

Trindade

Ochi

2006

Pesqui. Oper.

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ResumoO Problema de Formação de Células de Manufatura (PFCM) é uma questão central para um projeto de geração de células de produção em sistemas de manufatura. Este problema é basicamente descrito por um conjunto de partes de produtos e máquinas. O objetivo é o de construir clusters ou células de manufatura associando produtos com agrupamentos de máquinas. Este trabalho apresenta um novo algoritmo evolutivo híbrido para a solução do PFCM. São mostrados resultados computacionais do algoritmo proposto para um conjunto de instâncias disponíveis na literatura. Das 36 instâncias analisadas, o algoritmo proposto encontrou uma solução superior aos existentes em 8 casos e em 26 instâncias encontrou a mesma melhor solução da literatura. Palavras-chave: heurísticas; algoritmos evolutivos; algoritmos genéticos. AbstractThe Manufacturing Cell Formation Problem (MCFP) is a crucial component of a cell production design in a manufacturing system. This problem is composed by a set of parts of products to be manufactured and machines. The objective is to construct manufacturing clusters by associating products with cell machines. This paper presents a new hybrid evolutionary algorithm to solve the MCFP. Computational results with the proposed algorithm on a set of instances available in the literature are also presented. For 8 out of 36 instances considered, the propose method overcame the previous results from the literature and for 26 instances, the same best solutions were found.

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Manufacturing cell design: an integer programming model employing genetic algorithms

Cited by 115 publications

References 46 publications

A Comparison of a Standard Genetic Algorithm with a Hybrid Genetic Algorithm Applied to Cell Formation Problem

A Comparison of a Standard Genetic Algorithm with a Hybrid Genetic Algorithm Applied to Cell Formation Problem

Global Optimization of an Analog Method by Means of Genetic Algorithms

Um algoritmo evolutivo híbrido para a formação de células de manufatura em sistemas de produção

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