1. Modeling and Analysis of CONWIP-based Flowlines as Closed Queueing Networks

2. Topics Modeling CONWIP Flowlines as Closed Queueing Networks Implications of the derived results for the performance of the line –Performance bounds –Factors that affect the line performance –Strategies for enhancing the line performance –Explaining the “inertia” of CONWIP lines

3. CONWIP-based production lines Station 1Station 2Station 3 FGI Some important issues: What is the throughput attainable by a certain selection of target WIP level? What is the resulting cycle time? How do we select the target WIP level that will attain a desired production rate? How do the various operational detractors affect the performance of this line?

4. CONWIP flowlines as Closed Queueing Networks (CQNs) Approximate Mean Value Analysis: The underlying ideas I. Model a CONWIP line with its WIP level set to W as a CQN with W jobs II.The “PASTA” effect of CQNs with W jobs and exponential processing times: The expected number of jobs observed at the various stations by a job arriving at some station S j, is equal to the expected number of jobs observed at any random time at the same stations when the system is operated with W-1 jobs in it. III.Assume that the “PASTA” effect holds in an approximate sense for general distributions and develop an algorithm that will compute the performance measures of interest iteratively, for various W levels, starting with W=0. IV.The resulting method will work well for lines with single-server stations.  M1M2Mn

5. Notation n = number of stations t e (j) = mean effective processing time at station j c e 2 (j) = SCV for effective processing time at station j TH(W) = the line throughput when operated with WIP level W CT(W) = expected job cycle time through the line CT j (W) = expected job cycle time at station j when the WIP level is W WIP j (W) = expected WIP level at station j when the WIP level is W u j (W) = utilization of the server at station j when the WIP level is W

6. Deriving the algorithm iteration CT j (W) = E[remaining processing time for the job at the server of S j ] + (E[number of jobs at station S j ]-E[number of jobs in service])t e (j) + t e (j) But (a)E[remaining processing time for the job at the server of S j ] = Prob(Server of S j busy)  E[remaining process time | busy] = u j (W-1)  E[remaining process time | busy]  (b) E[number of jobs at station S j ]  WIP j (W-1) (c)E[number of jobs in service]  u j (W-1) (d) u j (W-1) = TH(W-1)  t e (j)

7. Deriving the algorithm iteration (cont.) Combining the results of the previous slide: But then, Obviously, for W=0, CT(0) =TH(0) = WIP j (0) = 0 Furthermore, application of the above formulae for W=1 gives: (from Little’s law)

8. The W-TH(W) space rbrb TH(W) W 1W o =r b T o 1/T o Ideal Operational Point

9. Deriving the upper bounds for TH(W) For W , TH(W)  r b  min j {1/t e (j)}, the bottleneck rate of the line. r b can also be achieved with finite WIP in a deterministic setting, i.e., in a line with c e (j) = 0,  j, and synchronized with pace t b =1/r b. However, by Little’s law, a line with raw process time T o, in order to produce at rate r b, will need a WIP level of W o =r b T o ; this WIP level is known as critical WIP. An interpretation of W o is given by the following formula: i.e., W o is the level of WIP that we must maintain in the system in order to maintain the bottleneck utilization at 100%. Otherwise, the bottleneck will starve. If W<W o, then in a deterministic setting we can pace the jobs through the system in such a way that CT=T o. Hence, the maximal line throughput will be

10. The W-CT(W) Space ToTo WoWo 1 CT(W) W ToTo 1/r b Ideal Operational Point

11. Practical Considerations The “ideal” performance is attained in an optimized, deterministic setting. Usually, the actual performance of the line will be compromised by –the variability inherent in the system operation –the impact of the applied control policies (e.g., the batching policy that provides the lower bound for TH(W))

12. Improving the System Performance The problem: Given a line operating at a desired throughput rate, TH, what are some possible mechanisms to reduce the expected cycle time through the line, CT (and through Little’s law, the line WIP, W) ? The key idea: We need to “pull” the curve describing the line performance in the W-TH(W) space to the left.

13. Mechanism I TH(W) W WoWo 1 TH W 1/T o rbrb rb’rb’ 1/T o ’ Wo’Wo’W’W’ Increase r b (by adding capacity or making more effective use of the existing capacity at the line bottleneck(s))

14. Mechanism II Add capacity to some non-bottleneck station(s) (this addition essentially enables the better catering to the bottleneck needs, but it can help only to a limited extent) TH(W) W WoWo 1 TH W 1/T o r b =r b ’ 1/T o ’ Wo’Wo’W’W’

15. Mechanisms III and IV Reduce the inherent variability at the different stations; the corresponding reduction of the station CVs will “pull” the performance curve in the W-TH(W) space closer to the curve characterizing the upper bound. Increase the line flexibility, which essentially enables the better utilization of the bottleneck capacity (and takes us back to item (i) above).

16. Demonstrating the “inertia” of CONWIP lines Problem: Consider a CONWIP line operated at 80% of its bottleneck rate. Furthermore, the performance of the line compares favorably to that of the “benchmark” case, and W>>W o so that  CT  W/r b. Compute the relative increase  W/W that will increase the line throughput to 85% of its bottleneck rate.

17. Demonstrating the “inertia” of CONWIP lines (cont.) Solution: Let  W=W’-W=xW. Then, i.e., the necessary increase is almost 42% of the original WIP!