swarm intelligence (si) for decision support of operations ... · process of pso-based resource...
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Swarm Intelligence (SI) for Decision
Support of Operations Management –
Methods and Applications
Dr. Yi WangThe University of Manchester, Department of engineering and physics, School of materials
Combining the strengths of UMIST andThe Victoria University of Manchester
The University of Manchester, Department of engineering and physics, School of materials
Oxford Road, Manchester, M13 9PL, UK
web-page: http://www.manchester.ac.uk
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Prof. Lilan Liu
Shanghai University
Yanchang Road 149. 200072, Shanghai, China
web-page: http://www.shu.edu.cn
Prof. Kesheng Wang
Norwegian University of Science and Technology
S. P. Andersensveien 5, 7014 Trondheim,
Norway
web-page: http://www.ntnu.no
Content
• Background
• Swarm intelligence
• Manufacturing grid
• Case study
Combining the strengths of UMIST andThe Victoria University of Manchester
• Case study
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Background
• Collaborative project
• Complex product
• Complex network
• Plan resource allocation
Combining the strengths of UMIST andThe Victoria University of Manchester
• Plan resource allocation
• Swarm intelligence
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Swarm intelligence
• The complexity and sophistication of
Self-Organization is carried out with
no clear leader
• What we learn about social insects
can be applied to the field of
Decision support
Combining the strengths of UMIST andThe Victoria University of Manchester
Decision support
• Model how social insects collectively
perform tasks
– Use this model as a basis upon
which artificial variations can be
developed
– Model parameters can be tuned
within a biologically relevant
range or by adding non-biological
factors to the model
Swarm intelligence
Combining the strengths of UMIST andThe Victoria University of Manchester
Ant Colony Optimization (ACO),Particle Swarm Optimization (PSO), Bees Colony Algorithms (BCO) and Stochastic Diffusion Search (SDS)5
Bird flocking of PSO
( )i
x t→
( ( ) ( ))i i
p t x t→ →
−
( ( ))g i
p x t→ →
−
Combining the strengths of UMIST andThe Victoria University of Manchester
( 1)ix t→
+( 1)
iv t→
+
( )i
v t→
( ) ( )1 1 2 2( 1) ( ) ( ) ( )id id id id gd id
v t v t c r p x t c r p x tω+ = ⋅ + − + −
( 1) ( ) ( 1)id id id
x t x t v t+ = + +
Grid Manufacturing
• Grid: interconnected mesh of operations and services
• Consumer essentially sees a single supply route for his needs
Combining the strengths of UMIST andThe Victoria University of Manchester
for his needs
• Collaboration towards common and different business goals
• Bring manufacturing resources together –sometimes these are distributed physically
• Focus on network efficiency and efficiency of stand alone operations
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Grid manaufacturing
Task
s
Reso
urce
disp
atch
er
Reso
urce
Managem
ent
Vario
us
Reso
urce
Publish Publish
Request Request Control
Task
s
Reso
urce
disp
atch
er
Reso
urce
Managem
ent
Vario
us
Reso
urce
Publish Publish
Request Request Control
The (dynamic) harnessing of significant, disparate
manufacturing capabilities and resources in order to
satisfy one or more business requirements
Combining the strengths of UMIST andThe Victoria University of Manchester
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Task
s
Reso
urce
disp
atch
er
Reso
urce
Managem
ent
Vario
us
Reso
urce
Customers Application
layer Resource
Response ResponseAllocation
Task
s
Reso
urce
disp
atch
er
Reso
urce
Managem
ent
Vario
us
Reso
urce
Customers Application
layer Resource
Response ResponseAllocation
Why establish a Manufacturing Grid? Establish or Evolve, or Design or Develop?
• Innovation challenges:– enable greater levels of customer response and customisation, especially,
unpredictable customer demands
– cope with faster and faster technology development
– cope with complexity of system
Combining the strengths of UMIST andThe Victoria University of Manchester
– cope with complexity of system
• Efficiency challenges:– maximise the use of existing manufacturing supply chain resources, and
dispersed expertise and professional services
– exploit existing developments in IT infrastructure, virtual enterprise
management, collaborative design etc
• Competition challenges:– complementary specialist network and quick adaptation
Case Study
Combining the strengths of UMIST andThe Victoria University of Manchester
Case Study
Process of PSO-based resource
allocationStart the iteration
10 particles be randomly
initialized
Update all particle with Eq. (8)
and Eq. (9)
Modify location of particles out
of bounds
Start the iteration
10 particles be randomly
initialized
Update all particle with Eq. (8)
and Eq. (9)
Modify location of particles out
of bounds
Combining the strengths of UMIST andThe Victoria University of Manchester
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Calculate the gbest and pbest
Move on iteration
Modify speed of particles over
the restrictions
End?
Get the result of optimization
yes
no
Calculate the gbest and pbest
Move on iteration
Modify speed of particles over
the restrictions
End?
Get the result of optimization
yes
no
Manufacturing grid evolution model
A new task1. Generating parameters
2. Confirming resources requirements
Generating one candidate group
for each required resource
respectively according to the
following
Probability p :
Choosing one from existing
types with a preference
Probability 1-p :
Adding new resource typeLooping until the N
candidate groups are
generated
A new resource type with
only one resource
Existing resources
meet the needs?
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Adding a new resource
to this type1. Probability 0.6: resource is provided by an existed
factory
2. Probability 0.4: resource is provided by a new factory
YN
Probability 1-q :
Selecting in existing
resources
Probability q :
Adding new resources
Adding all resources meeting the requirements to the
candidate group
Forming a
candidate
group
Statistics
and Analysis
Moving to
the next
loop
N
Y
PSO-based
Resource
Allocation Model
End？N
YN candidates groups
have been selected?
The economic objectives for
resource allocation– Minimizing average processing cost
jRP is the processing cost of resource ( 1, , )
jR j n= L to accomplish sub-tasks ( 1, , )
i
sT i n= L .
Combining the strengths of UMIST andThe Victoria University of Manchester
– Minimizing average logistics cost
Which, jtP is the logistics cost caused by the distance of each two resources
( ; , 1, , .)xy
R x y x y n< = L to accomplish sub-task ( 1, , )i
sT i n= L .
PSO-based multi-objective Resource allocation
• Task Economics objective
• System Robustness Objective
Combining the strengths of UMIST andThe Victoria University of Manchester
• Evaluation Function
Factory collaboration network
structure
1000 tasks (460 factories)
Combining the strengths of UMIST andThe Victoria University of Manchester
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3000 tasks (1264 factories)
5000 tasks (1720 factories)
Conclusion
• Swarm intelligence has the ability to collectively solve very complex problems
• A view of SI applications in OM with a specific focus on optimization problems for better decision support.
Combining the strengths of UMIST andThe Victoria University of Manchester
better decision support.
• The future research should focus of the development of perception-based modelling, and self-organized production, schedule and control.
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Thank you !!!
Combining the strengths of UMIST andThe Victoria University of Manchester
Thank you !!!