christian hammel technische universit t dresden matthias sch ps globalfoundries dresden n.
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Network Optimization prior to Dynamic Simulation of AMHS

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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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Presentation Transcript
agenda
Agenda
  • Introduction
  • Network model basics
  • Optimization approach
  • Application areas
  • Case study:
    • Introduction
    • Simulation
    • Results
routing in complex amhs
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

common approach
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
network approach
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
track utilization
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)
traffic distribution
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
algorithm
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
simulation
Simulation

= ?

  • Network optimization prior to dynamic simulation of AMHS
  • Gained insights from network analysis also help interpreting simulation behaviour and results
application
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
gf fab1 module1
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
iteration process
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)
validation by simulation
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

real system implementation
Real System Implementation

transports / h

DT in mins

transport load

performance of AMHS

date of change

Impact on transport performance

summary
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
thank you for your attention
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