A group genetic algorithm for the machine cell formation problem

Filho, Eduardo Vila Gonçalves; Tiberti, Alexandre José

doi:10.1016/j.ijpe.2004.12.029

Cited by 60 publications

(9 citation statements)

References 16 publications

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“…indicating that the MC doesn't locate in row of layout area; multiobjective optimization, nonlinear programming and constrained function optimization [17,18]. In recent years, GA has successfully implemented CF and layout problems [19][20][21][22]. Since the fuzzy parameters are included in the model of the flexible numerically controlled MCs, fuzzy simulation is used to obtain the optimal solution.…”

Section: =1mentioning

confidence: 99%

Layout Optimization of Flexible Manufacturing Cells Based on Fuzzy Demand and Machine Flexibility

Zhang

Zhou

Zhao

2018

Mathematical Problems in Engineering

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Layout flexibility is critical for the performance of flexible manufacturing cells, especially in dynamic production environment. To improve layout flexibility, layout optimization should consider more flexible factors based on existed models. On the one hand, not only should the current production demands be covered, but also the future uncertain demands should be considered so that the cell can adapt to the dynamic changes in a long term. On the other hand, the flexibility of machines should be balanced in the layout in order to guarantee that the cell can deal with dynamic new product introduction. Starting from these two points, we formulate a layout optimization model based on fuzzy demand and machine flexibility and then develop a genetic algorithm with bilayer chromosome to solve the model. We apply this new model to a flexible cell of shell products and test its performance by comparing it with the classical two-stage model. The total logistics path of the new model is shown to be significantly shorter than the classical model. Then we carry out adaptability experiments to test the flexibility of the new model. For the dynamic situation of both the fluctuation of production demands and the introduction of new products, the new model shows obvious advantages to the classical model. The results indicate that this advantage becomes greater as the dynamics becomes greater, which implies that considering fuzzy demand and machine flexibility is necessary and reasonable in layout optimization, especially when the dynamics of the production environment is dramatic.

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Section: =1mentioning

confidence: 99%

Layout Optimization of Flexible Manufacturing Cells Based on Fuzzy Demand and Machine Flexibility

Zhang

Zhou

Zhao

2018

Mathematical Problems in Engineering

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“…Yu and Sarker (2006) proposed a quadratic assignment problem model to minimize the total intercellular flows by considering bottleneck parts in which the output of other group formation methods can be used as an input of their method. Goncalves Filho and José Tiberti (2006) proposed a GA to minimize number of inter-cellular material transferring and cell load variations. Nsakanda et al (2006) presented a multi-process plan problem to determine the best part routing of each process plan.…”

Section: Fig 1 Intracellular and Intercellular Materials Transferringmentioning

confidence: 99%

“…On the basis of the review of existing literature, heuristics and metaheuristics have been widely employed by scientists to determine optimum or nearoptimum solutions. Genetic Algorithm has been successfully used in similar researches in CMS and therefore can be considered as a trustful method for solving the proposed model (Banerjee & Das, 2012;Dimopoulos & Mort, 2001;Goncalves Filho & José Tiberti, 2006;Paydar et al, 2013;Rogers & Kulkarni, 2005). The risk of trapping in the local optima is high because of the complexity of the proposed model.…”

Section: Solution Proceduresmentioning

confidence: 99%

A new method for decreasing cell-load variation in dynamic cellular manufacturing systems

Delgoshaei¹,

Ariffin²,

Baharudin³

et al. 2016

10.5267/j.ijiec

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Cell load variation is considered a significant shortcoming in scheduling of cellular manufacturing systems. In this article, a new method is proposed for scheduling dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim of this method is to control cell load variation during the process of determining the best trading off values between in-house manufacturing and outsourcing. A genetic algorithm (GA) is developed because of the high potential of trapping in the local optima, and results are compared with the results of LINGO ® 12.0 software. The Taguchi method (an L_9 orthogonal optimization) is used to estimate parameters of GA in order to solve experiments derived from literature. An in-depth analysis is conducted on the results in consideration of various factors, and control charts are used on machine-load variation. Our findings indicate that the dynamic condition of product demands affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. An increase in product uncertainty level causes the loading level of each cell to vary, which in turn results in the development of "complex dummy sub-cells". The effect of the complex sub-cells is measured using another mathematical index. The results showed that the proposed GA can provide solutions with limited cell-load variations.

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“…Yu & Sarker (2006) proposed a quadratic assignment problem model to minimize the total intercellular flows by considering bottleneck parts in which the output of other group formation methods can be used as an input of their method. Goncalves Filho and José Tiberti (2006) proposed a GA to minimize number of inter-cellular material transferring and cell load variations. Afterward, Haleh et al (2009) presented a hybrid Memetic algorithm and revised TOPSIS method to minimize cell load variation and inter-cellular WIP transferring.…”

Section: Introductionmentioning

confidence: 99%

Evaluating impact of market changes on increasing cell-load variation in dynamic cellular manufacturing systems using a hybrid Tabu search and simulated annealing algorithms

Delgoshaei¹,

Parvin²,

Ariffin³

2016

10.5267/j.dsl

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In this paper, a new method is proposed for scheduling dynamic cellular manufacturing systems (D-CMS) in the presence of uncertain product demands. The aim of this method is to control the process of trading off between in-house manufacturing and outsourcing while product demands are uncertain and can be varied from period to period. To solve the proposed problem, a hybrid Tabu Search and Simulated Annealing are developed to overcome hardness of the proposed model and then results are compared with a Branch and Bound and Simulated Annealing algorithms. A Taguchi method (L_27 orthogonal optimization) is used to estimate parameters of the proposed method in order to solve experiments derived from literature. An in-depth analysis is conducted on the results in consideration of various factors. For evaluating the system imbalance in dynamic market demands, a new measuring index is developed. Our findings indicate that the uncertain condition of market demands affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. The results showed that the proposed hybrid method can provide solutions with better quality.

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A group genetic algorithm for the machine cell formation problem

Cited by 60 publications

References 16 publications

Layout Optimization of Flexible Manufacturing Cells Based on Fuzzy Demand and Machine Flexibility

Layout Optimization of Flexible Manufacturing Cells Based on Fuzzy Demand and Machine Flexibility

A new method for decreasing cell-load variation in dynamic cellular manufacturing systems

Evaluating impact of market changes on increasing cell-load variation in dynamic cellular manufacturing systems using a hybrid Tabu search and simulated annealing algorithms

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