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Network Optimization prior to Dynamic Simulation of AMHS

Christian Hammel, Technische Universität Dresden Matthias Schöps, Globalfoundries Dresden. Network Optimization prior to Dynamic Simulation of AMHS. Agenda. Introduction Network model basics Optimization approach Application areas Case study: Introduction Simulation Results.

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Network Optimization prior to Dynamic Simulation of AMHS

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  1. Christian Hammel, Technische Universität Dresden Matthias Schöps, Globalfoundries Dresden Network Optimization prior to Dynamic Simulation of AMHS

  2. Agenda • Introduction • Network model basics • Optimization approach • Application areas • Case study: • Introduction • Simulation • Results

  3. Routing in complex AMHS • Mainly based on shortest paths • Mainly static as availability of information is insufficient for dynamic approach • Risk of congestions even without failures shortest path

  4. Common Approach • Manual adjustments of routing • using dynamic simulations • only in selected points • expensively developed and tested • No holistic approach feasible • The bigger the system gets the more time-consuming and difficult this approach

  5. Network Approach source / sink nodes AMHStrack ZFS step 1 step 2 source / sink information attached to links toolports inter-section node links toolqueues • Transfer AMHS  network model • Shortest paths easy to find, sophisticated algorithms • No dynamic behaviour

  6. Track Utilization high sources linkutilization mid sinks low • Average transports per unit of time  transports as flows • Idea: limit utilization, lower than technical limit because of dynamic behaviour • If all tracks keep this limit: • Congestions because of traffic should be rare • Impacts of failures should be lower (higher robustness)

  7. Traffic Distribution high linkutilization mid low sources sinks • Virtually adjusting lengths (=costs) of links enables manipulating routing with no or minor software changes (and without hardware changes) • Analytic approach to keep all limits not feasible because of run time • Iterative algorithm increasing costs of over-utilized links

  8. Algorithm high utilization mid low + $ • Iteratively increase costs of over-utilized links • Possibilities: • One by one • All over-utilized links at once • Amount to increase depending on over-utilization

  9. Simulation = ? • Network optimization prior to dynamic simulation of AMHS • Gained insights from network analysis also help interpreting simulation behaviour and results

  10. Application Large and complex transport networks • New / adjusted transport layouts • Evaluation of layout alternatives • Analysis of max. TP / bottlenecks • Existing transport systems • Performance improvement without physical modification • Case Study

  11. GF Fab1 Module1 • 51 Stocker with 8120 storage bins • ZFGs with up to 2850 storage bins • Cleanroom area • 14,000m² at level3 • 2,000m² at level1 (Test+metrology area) • Tools direct deliverable by AMHS • 740 at level3 • 15 at level1 • AMHS is ~10 years old system from Murata • ~6.5 km of track • 280 Vehicle (235 then) • ~850 intersections

  12. Iteration Process - 220 tph - 110 tph - 0 tph Iterativelychangingcost factors • Calculate track utilization by adding shortest paths • Increase costs of most used pieces of track (depending on amount of utilization lowering and of mean shortest path length increase)

  13. Validation by Simulation Change in averagetraveldistance: + 4.8 % Change in 95-percentile ofdelivery time in sim.: +/- 0% .. – 20% Change in maximumthroughput in simulation: + 10.9 % Model impact to AMHS by dynamic simulation Original setting Adjusted cost setting

  14. Real System Implementation transports / h DT in mins transport load performance of AMHS date of change Impact on transport performance

  15. Summary • Network approach for traffic distribution in large transport systems • Providing further insight into system behaviour • More general system optimization possible because of • Shorter run time than dynamic simulation • Algorithm is distributing traffic by static routes • Throughput increase by changing routes without physical system modification • No negative impact to transport times

  16. Thank you for your attention! Network Optimization prior to Dynamic Simulation of AMHS Christian Hammel, Technische Universität DresdenTel.: +49 351 463 32539E-mail: christian.hammel@tu-dresden.deMatthias Schöps, Globalfoundries DresdenTel.: +49 351 277 3255E-Mail: matthias.schoeps@globalfoundries.com

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