Christian hammel technische universit t dresden matthias sch ps globalfoundries dresden
This presentation is the property of its rightful owner.
Sponsored Links
1 / 16

Network Optimization prior to Dynamic Simulation of AMHS PowerPoint PPT Presentation


  • 88 Views
  • Uploaded on
  • Presentation posted in: General

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.

Download Presentation

Network Optimization prior to Dynamic Simulation of AMHS

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Christian hammel technische universit t dresden matthias sch ps globalfoundries dresden

Christian Hammel, Technische Universität Dresden Matthias Schöps, Globalfoundries Dresden

Network Optimization prior to Dynamic Simulation of AMHS


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:[email protected] Schöps, Globalfoundries DresdenTel.: +49 351 277 3255E-Mail:[email protected]


  • Login