1. Queueing network modelling of flexible manufacturing system using mean value analysis Presented By: YAMIL PEREZ Authors: M.Jain, Sandhya Maheshwari, K.P.S. Baghel Accepted: February 16, 2007

2. 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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5. Relation to the Course

6. Parameters

7. Design Description (p.702) Assumption: Stations cannot wait for the MHD

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

9. 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): N=num.of pallets / m=pallets waiting in the queue for MHD M/M/C/N queueing model. Reference [29] Using this model, the service rate (λ) for the pallet can be found, and hence the average waiting time in the queue or MHD.

10. Design Principle Cont. Algorithm to calculate the average waiting time (Wr) of MHD.

11. Results Adaptive Neuro-Fuzzy System (ANFS) network -Fuzzy toolbox in MATLAB used for approximating T r, X and W r. -The performance measures are found by varying different system parameters: C, δ, 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:

12. Results Cont. Parameters: N=24,S=15, Q=5 As the number of MHD (C) increases, the throughput (X) and average time (W r ) decreases.

13. Results Cont. Parameters: N=24, δ =15, Q=5 -Effect of S on X and Wr for C=1 and C=5 -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 C=1 compared to C=5

14. Results Cont. Parameters: S=15, δ =0.25, Q=5 -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

15. Results Cont. -Result of mean service time (T r ) and (W r ) by varying the move time multiplier (δ). -As δ increases, T r increases linearly -The waiting time (W r ) increases exponentially as δ also increases.

16. 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.

17. 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

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

19. Industries Most Impacted Any industry that implements FMS