Queueing network modelling of flexible manufacturing system using mean value analysis Presented By: YAMIL PEREZ

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Queueing network modelling of flexible
manufacturing system using mean value
analysisPresented By YAMIL PEREZ

• Authors M.Jain, Sandhya Maheshwari, K.P.S.
  Baghel
• Accepted February 16, 2007

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Function of Paper

• Develop a queuing model to predict the
  performance of FMS using multiple discrete
  material handling devices (MHD).

Importance
Provide an insight on how manufacturing systems
can be upgraded today by improving the throughput
and reduction in expected waiting time.
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References

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Relation to the Course
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Parameters
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Design Description (p.702)
Assumption Stations cannot wait for the MHD
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Design Principle

• Algorithm used for calculating the throughput (X)
  of the material-handling device (MHD) based on
  Mean Value Analysis. (p.703)

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Design Principle Cont.

• Queueing Network Model
• Pallets come out from a blackbox and wait for
  service in a queue where the MHD serves each
  pallet in FIFO fashion.
• Request for service are made by the pallets which
  are in the blackbox.
• Assumption rate at which pallet arrives from the
  blackbox is a function of (N-m)
• Nnum.of pallets / mpallets waiting in the queue
  for MHD
• M/M/C/N queueing model. Reference 29
• Using this model, the service rate (?) for the
  pallet can be found, and hence the average
  waiting time in the queue or MHD.

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Design Principle Cont.

• Algorithm to calculate the average waiting time
  (Wr) of MHD.

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Results

• Adaptive Neuro-Fuzzy System (ANFS) network
• Fuzzy toolbox in MATLAB used for approximating
  Tr, X and Wr.
• The performance measures are found by varying
  different system parameters C, d, N and S.
• Using these parameters as inputs, an ANFS network
  was built.
• ANFS measure values serve as a comparison for the
  analytical values calculated using MVA.
• Fix set of Homogeneous and Heterogeneous
  processing times for fuzzy and MVA measurements

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Results Cont.

• Parameters N24,S15, Q5
• As the number of MHD (C) increases, the
  throughput (X) and average time (Wr) decreases.

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Results Cont.

• Parameters
• N24, d 15, Q5
• Effect of S on X and Wr for C1 and C5
• X and Wr increase with increasing S.
• However, for heterogeneous processing time these
  take lower values in comparison to the
  homogeneous one.
• Both X and Wr are larger for C1 compared to C5

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Results Cont.

• Parameters
• S15, d 0.25, Q5
• - As N increases, both X and Wr decrease
• - For homogeneous values of processing time these
  take the higher value in comparison to the
  heterogeneous one

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Results Cont.

• Result of mean service time (Tr) and (Wr) by
  varying the move time multiplier (d).
• As d increases, Tr increases linearly
• The waiting time (Wr) increases exponentially as
  d also increases.

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Correlation of Results with Model

• The results obtained by adaptive neuro-
• fuzzy inference systems are in good
• agreement with the numerical results
• obtained using MVA algorithm.

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Practical Use

• Improvement of the throughput time and reduction
  in expected waiting time help in upgrading
  existing manufacturing systems
• Minimize the total cost of FMS in the long run

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Technical Advancement

• The use of neuro-fuzzy techniques for developing
  approximations for complex problems.
• The use of queueing network modeling for FMS
  quantitative analysis

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Industries Most Impacted

• Any industry that implements FMS