Title: A Genetic Algorithm Tool for Designing Manufacturing Facilities in the Capital Goods Industry
1A 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
2Capital 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.
3Capital goods company activities
4Types of Facilities Design Problems
- Green field designer free to select processes,
machines, transport, layout, building and
infrastructure - Brown field existing situation imposes many
constraints
5Facilities 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
6Cell 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
7Rank Order Clustering Applied to data Obtained
from a capital goods company
8Similarity Coefficient
9Agglomerative clustering using the single linkage
strategyEquation 1
10Agglomerative clustering with complete linkage
strategy
11Clustering 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.
12Genetic Algorithm Design Tool
- Based upon
- Manufacturing System Simulation Model (Hicks
1998) - GA scheduling tool (Pongcharoen et al. 2000)
13(No Transcript)
14GA 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.
15Genetic Algorithm
Similar to Pongcharoen et al except, the repair
process is different and it is implemented in
Pascal
16Placement Algorithm
17Case Study
- 52 Machine tools
- 3408 complex components
- 734 part types
- Complex product structures
- Total distance travelled
- Direct distance 232Km
- Rectilinear distance 642Km
18Initial facilities layout
19Total rectilinear distance travelled vs.
generation (green field)
20Resultant Brown-field layout
21Total rectilinear distance vs. generation (green
field)
Note the rapid convergence with lower totals
than for the brown field problem
22Resultant 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.
23Conclusions
- 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.
24Future 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.