A Genetic Algorithm Tool for Designing Manufacturing Facilities in the Capital Goods Industry - PowerPoint PPT Presentation

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A Genetic Algorithm Tool for Designing Manufacturing Facilities in the Capital Goods Industry

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Few natural machine-part clusters ... GA Procedure. Use GAs to create sequences of machines ... The integration with a GA scheduling tool provides a mechanism for ... – PowerPoint PPT presentation

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Title: A Genetic Algorithm Tool for Designing Manufacturing Facilities in the Capital Goods Industry


1
A Genetic Algorithm Tool for Designing
Manufacturing Facilities in the Capital Goods
Industry
  • Dr Christian Hicks,
  • University of Newcastle,
  • England
  • Email Chris.Hicks_at_ncl.ac.uk

2
Capital Goods Companies
  • Complex products e.g. turbine generators,
    oilrigs, cranes
  • Complex processes including component
    manufacturing, assembly, construction and
    commissioning
  • Highly customised designs
  • Very low volume production with highly variable
    demand.

3
Capital goods company activities
4
Types of Facilities Design Problems
  • Green field designer free to select processes,
    machines, transport, layout, building and
    infrastructure
  • Brown field existing situation imposes many
    constraints

5
Facilities Layout Problem
  • Includes
  • Job assignment selection of machines for each
    operation and definition of operation sequences
  • Cell formation assignment of machine tools and
    product families to cells
  • Layout design geometric design of manufacturing
    facilities and the location of resources
  • Transportation system design
  • This paper considers cell formation and layout
    design

6
Cell Formation Methods
  • Eyeballing
  • Coding and classification
  • Product Flow Analysis
  • Machine-part incidence matrix methods
  • Rank Order Clustering
  • Close Neighbour Algorithm
  • Agglomerative clustering
  • Various similarity coefficients
  • Alternative clustering strategies

7
Rank Order Clustering Applied to data Obtained
from a capital goods company
8
Similarity Coefficient
9
Agglomerative clustering using the single linkage
strategyEquation 1
10
Agglomerative clustering with complete linkage
strategy
11
Clustering applied to capital goods companies
  • Limitations
  • Few natural machine-part clusters
  • Long and complex routings mitigate against self
    contained cells
  • Clustering only uses routing information
  • Geometric information is not used.

12
Genetic Algorithm Design Tool
  • Based upon
  • Manufacturing System Simulation Model (Hicks
    1998)
  • GA scheduling tool (Pongcharoen et al. 2000)

13
(No Transcript)
14
GA Procedure
  • Use GAs to create sequences of machines
  • Apply a placement algorithm to generate layout.
  • Measure total direct or rectilinear distance to
    evaluate the layout.

15
Genetic Algorithm
Similar to Pongcharoen et al except, the repair
process is different and it is implemented in
Pascal
16
Placement Algorithm
17
Case Study
  • 52 Machine tools
  • 3408 complex components
  • 734 part types
  • Complex product structures
  • Total distance travelled
  • Direct distance 232Km
  • Rectilinear distance 642Km

18
Initial facilities layout
19
Total rectilinear distance travelled vs.
generation (green field)
20
Resultant Brown-field layout
21
Total rectilinear distance vs. generation (green
field)
Note the rapid convergence with lower totals
than for the brown field problem
22
Resultant layout (green field)
Note that brown field constraints, such as
walls Have been ignored. The solution is not
realistic because there is insufficient space
for materials.
23
Conclusions
  • Significant body of research relating to
    facilities layout, particularly for job and flow
    shops.
  • Much research related to small problems.
  • Capital goods companies very complex due to
    complex routings and subsequent assembly
    requirements.
  • Clustering methods are generally inconclusive
    when applied to capital goods companies.
  • GA tool shows an improvement of 55 in the green
    field case and 30 in the brown field case.

24
Future Work
  • The GA layout generation tool is embedded within
    a large sophisticated simulation model.
  • Dynamic layout evaluation criteria can be used.
  • The integration with a GA scheduling tool
    provides a mechanism for simultaneously
    optimising layout and schedules.
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