Neural Network and Cellular Manufacturing

Jamal, Amani

doi:10.1108/02635579310027343

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…Constraints (9) take into account the set of machines cannot be located in the same cell and Constraints (10) do the same for the set of machines should be located in the same cell. Constraints (11) confirms the relationship of machine types in every process route and the number of their corresponding operations, while used variables are defined by Constraints (12) and (13).…”

Section: Model Formulationmentioning

confidence: 99%

A new approach towards integrated cell formation and inventory lot sizing in an unreliable cellular manufacturing system

Rafiee

Rabbani

Rafiei

et al. 2011

Applied Mathematical Modelling

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a b s t r a c tThis paper presents a comprehensive mathematical model for integrated cell formation and inventory lot sizing problem. The proposed model seeks to minimize cell formation costs as well as the costs associated with production, while dynamic conditions, alternative routings, machine capacity limitation, operations sequences, cell size constraints, process deterioration, and machine breakdowns are also taken into account. The total cost consists of machine procurement, cell reconfiguration, preventive and corrective repairs, material handling (intra-cell and inter-cell), machine operation, part subcontracting, finished and unfinished parts inventory cost, and defective parts replacement costs. With respect to the multiple products, multiple process plans for each product and multiple routing alternatives for each process plan which are assumed in the proposed model, the model is combinatorial. Moreover, unreliability conditions are considered, because moving from ''in-control" state to ''out-of-control" state (process deterioration) and machine breakdowns make the model more practical and applicable. To conquer the breakdowns, preventive and corrective actions are adopted. Finally, a Particle Swarm Optimization (PSO)-based meta-heuristic is developed to overcome NP-completeness of the proposed model.

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Section: Model Formulationmentioning

confidence: 99%

A new approach towards integrated cell formation and inventory lot sizing in an unreliable cellular manufacturing system

Rafiee

Rabbani

Rafiei

et al. 2011

Applied Mathematical Modelling

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“…Kusiak [15] discussed a knowledge-based group technology system. Jamal [12] proposed the use of neural networks to solve different types of cellular manufacturing problems. More recent development of applying such non-traditional methods in cellular manufacturing includes Liao [17], Chen and Cheng [6], Rao and Gu [20], and Bahrami et al [3].…”

Section: Introductionmentioning

confidence: 99%

Untitled

Chen¹

1998

Annals of Operations Research

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In a dynamic manufacturing environment, manufacturing cell configurations based on current part mix and production process may need to be revised once the part mix or the production process has changed. However, machine and equipment moving costs make frequent reconfiguration uneconomical and sometimes impossible. Designing a sustainable cellular manufacturing system in a dynamic environment is studied in this paper. An integer programming model is developed to minimize material handling and machine costs as well as cell reconfiguration cost for a planning horizon of multiple time periods. Solving this integer programming problem is NP-complete. A decomposition approach is developed so that the decomposed subproblems can be solved with less computational effort. Dynamic programming is then employed to find a solution of the original problem. Numerical examples are presented to illustrate the model and the solution technique developed in this paper.

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“…The network with the trained weights is then employed as the basis for classifying new inputs. The most popular technique of this type is the back propagation algorithm [137][138][139][140].…”

Section: Neural Networkmentioning

confidence: 99%

Virtual production system

Ko¹

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This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis arxl dissertation copies are in typewriter face, wtiile ottiers may be from any type of computer printer.The quality of this reproduction is dependent upon tlw quality of ttie copy submitted. Brolwn or irvlistinct print cotored or poor quality illustrations and photographs, print bleedUirough, substandard margins, and improper alignment can adversely aftect reproduction.In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these «mN be noted. Also, if unauthorized copyright material had to t)e removed, a rwte will indicate the deletion.Oversize materials (e.g., maps, drawings, charts) are reproduced t»y sectioning the original, k)eginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps.

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Neural Network and Cellular Manufacturing

Cited by 9 publications

References 9 publications

A new approach towards integrated cell formation and inventory lot sizing in an unreliable cellular manufacturing system

A new approach towards integrated cell formation and inventory lot sizing in an unreliable cellular manufacturing system

Untitled

Virtual production system

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