a genetic algorithm tool for designing manufacturing facilities in the capital goods industry

© C.Hicks, University of Newcastle

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A Genetic Algorithm Tool for Designing Manufacturing

Facilities in the Capital Goods Industry

Dr Christian Hicks,

University of Newcastle,

England

Email: [email protected]


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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


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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


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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


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Rank Order Clustering Applied to data Obtained from a capital goods company


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Similarity Coefficient

1

654

32

S ij = m ax(n ij/n i, n ij/n j)

S2,5 = m ax(2 /3 , 2 /2)

S2,5 = 1


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Agglomerative clustering using the singlelinkage strategyEquation 1


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Agglomerative clustering with complete linkage strategy


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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.


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Genetic Algorithm Design Tool

Based upon: • Manufacturing System Simulation

Model (Hicks 1998) • GA scheduling tool (Pongcharoen et

al. 2000)


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Manufacturing Planing &Control System

Manufacturing Facility

Manufacturing System Simulation Model

Planned Schedule

Resourceinformation

CAPM modules used

System parameters

Product information

Operational factors

System dynamics Logic

Measures ofperformance

Flow measurementCluster AnalysisLayout generation methods

Tools


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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.


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Genetic Algorithm

Similar to Pongcharoen et al except, the repair process is different and it is implemented in Pascal


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Placement Algorithm


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Case Study

• 52 Machine tools• 3408 complex components• 734 part types• Complex product structures• Total distance travelled

– Direct distance 232Km

– Rectilinear distance 642Km


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Initial facilities layout


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Total Rectilinear Distance vs Generation

0

100000

200000

300000

400000

500000

600000

700000

800000

1 11 21 31 41 51 61 71 81 91 101

111

121

131

141

151

161

171

181

191Generation

Tota

l Rec

tilin

ear

Dis

tan

ce (

m)

Minimum

Average

Population size 200Generations 200Crossover 90%Mutation 18%

Total rectilinear distance travelled vs. generation (brown field)


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Resultant Brown-field layout


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Total rectilinear distance vs. generation (green field)

Total rectilinear distance travelled vs. generation(green field problem)

0

100000

200000

300000

400000

500000

600000

700000

800000

1

11 21

31

41

51

61

71

81

91

10

1

111

12

1

13

1

14

1

15

1

16

1

17

1

18

1

19

1

Generation

To

tal

rec

tili

ne

ar

dis

tan

ce

(m

)

Average

Minimum

Note the rapid convergence with lower totals than for the brown field problem


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Resultant layout (green field)

Note that brown field constraints, such as wallsHave been ignored.


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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 70% in the green field case and 30% in the brown field case.


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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.

a genetic algorithm tool for designing manufacturing facilities in the capital goods industry

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