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© C.Hicks, University of Newcastle
HIC288/1
A TOOL FOR OPTIMISING FACILITIES DESIGN FOR CAPITAL GOODS COMPANIES
Christian HicksEmail: [email protected]
University of Newcastle,
England.
http://www.staff.ncl.ac.uk/chris.hicks/presentations/presin.htm
© C.Hicks, University of Newcastle
HIC288/2
Capital Goods Companies• Products and processes usually complex.• Typical products include steam turbines for power
generation, oil rigs and bespoke cranes.• Production facilities include jobbing, batch, flow and
assembly systems.• Customised to meet individual customer requirements.• Engineered-to-order.• Low volume, ‘lumpy’, erratic demand.
© C.Hicks, University of Newcastle
HIC288/4
© C.Hicks, University of Newcastle
HIC288/5
Facilities Design Problems• Block plans show the relative positioning of resources.• Plans may be evaluated in terms of static measures
e.g. total distance travelled by components.• Problems may be classified as:
– Green field – designer free to select processes, machines, transport, layout, building and infrastructure;
– Brown field – existing situation imposes many constraints.
© C.Hicks, University of Newcastle
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Genetic Algorithm Tool• Based upon an analogy with biological evolution in
which the fitness of an individual determines its ability to survive and reproduce.
• Uses GAs to create sequences of machines or ‘chromosomes’.
• Applies a placement algorithm to generate layouts.• Evaluates layouts in terms of total direct or rectilinear
distance to determine ‘fitness’.• The probability of ‘survival’ of a chromosome to the
next generation is a function of its ‘fitness’
Genetic Algorithm Procedure
Start Encode GenesChromosome
Chromosome
Chromosome
Ran
dom
ly c
ombi
ne g
enes
Crossover Function
Parent 1
Parent 2
X
Offspring 1
Offspring 1
Parent 1 Offspring 1
Mutation Function
Genetic Operators
Ran
dom
ly s
elec
t chr
omos
omes
Check and eliiminateduplication
Produce layout usingplacemenrt algorithm with
constraint checking
Evaluate "fitness" in termsof total direct / rectilinear
distance travelled
RouletteWheel
Stop
Terminate ?
Display
Create population forgenerationYes
No
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Placement Algorithm
© C.Hicks, University of Newcastle
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Case Study• Heavy engineering job shop.• 52 Machine tools.• 3408 complex components.• 734 part types.• Complex product structures.• Total distance travelled:
– Direct distance 232Km;
– Rectilinear distance 642Km.
© C.Hicks, University of Newcastle
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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
191
Generation
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)
© C.Hicks, University of Newcastle
HIC288/12
Resultant brown-field layout
© C.Hicks, University of Newcastle
HIC288/13
Total rectilinear distance travelled vs. generation (green field)
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
191
Generation
To
tal r
ecti
linea
r d
ista
nce
(m
)
Average
Minimum
© C.Hicks, University of Newcastle
HIC288/14
Resultant green field layout
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, but much of the research is related to small problems.
• Capital goods companies utilise flow, cellular, jobbing and assembly systems.
• Job shops incorporate most capital intensive plant and produce the highest value, longest lead-time items.
• GA tool generated layout reduces total rectilinear distance travelled by 25% for the brown field case.
© C.Hicks, University of Newcastle
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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 with respect to static and dynamic performance criteria.
© C.Hicks, University of Newcastle
HIC288/17
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
