Ubiquitous Optimisation
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Ubiquitous OptimisationUbiquitous Optimisation
Making Optimisation Easier to Use
Prof Peter Cowling
http://www.mosaic.brad.ac.uk
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Optimisation in Decision Optimisation in Decision MakingMaking
Uncontrollable factors
DDeessiirraabbiilliittyy
Current situation
D1
D2
D3
D4
Controllablefactors
Outcomes
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ModellingModelling
•Ill-structured
•Complex
•Abstract
•Well-structured
•Simple
•Concrete
Model
Conceptual Model
Tangiblesystem
Creation
Testing
Reflection
Extraction
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OptimisationOptimisation NP-NP-hardhard
Evolutionary Algorithms
Artificial Intelligence
Operational Research
Novel Ideas
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Does it work?Does it work?
• Oil companies could not survive without optimisation
• Manufacturing/transport/logistics/ project management – productivity improvements in the £billions worldwide
• Widely and expensively used in finance and management consultancy
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Ubiquitous?Ubiquitous?
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BeneficiariesBeneficiaries• Any manager or engineer and every
decision could benefit from a system which brought useful and usable optimisation.
• Consider the proliferation of spreadsheet use among managers/ engineers.
• The potential productivity improvements are in the £00,000,000,000s – from improved resource usage, better market targetting, better financial management.
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Advances which may bring Advances which may bring ubiquitous optimisation ubiquitous optimisation
closercloser• Speech/gesture input/output• Intelligent, learning computers• Cognitive science advances• Ambient computing• Control/sensor technologies• Increased IT awareness among
managers/engineers
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Angles of attackAngles of attack• Hyperheuristics, Software Toolboxes
– Reducing the effort and expertise to model and solve problems
• Human-computer interaction and cognitive science– Integrating human and artificial intelligence
• Dynamic Optimisation – Stability and Utility– Reacting to the dynamic nature of real
problems
• Gaining real-world problem experience
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HyperheuristicsHyperheuristics
L.L. Heuristic performance
HyperheuristicHeuristic
Choice
Low level heuristics
Problem
Solution quality
Solution perturbation
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Benefits of HyperheuristicsBenefits of Hyperheuristics
• Low level heuristics easy to implement
• Objective measures may be easy to implement – they should be present to raise decision quality
• Rapid prototyping – time to first solution low
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Concrete exampleConcrete example
• Organising meetings at a sales summit
• Low level heuristics:– Add meeting, delete meeting, swap
meeting, add delegate, remove delegate, etc.
• Objectives:– Minimise delegates – Maximise supplier meetings
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Concrete ExampleConcrete Example
• Hyperheuristic based on the exponential smoothing forecast of performance, compared to simple restarting approaches
• Result: 99 delegates reduced to 72 delegates with improved schedule quality for both delegates and suppliers
• Compares favourably with bespoke metaheuristic (Simulated Annealing) approach
• Fast to implement and easy to modify
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Other applicationsOther applications
• Timetabling mobile trainers• Nurse rostering• Scheduling project meetings• Examination timetabling
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Other HyperheuristicsOther Hyperheuristics
• Genetic Algorithms– Chromosomes represent sequences of
low level heuristics– Evolutionary ability to cope with
changing environments useful• Forecasting approaches• Genetic Programming approaches• Artificial Neural Network
approaches
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Human-Computer Human-Computer InteractionInteraction
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STARK diagramsSTARK diagrams
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Representing constraints Representing constraints Room capacity violation
Period limit violation
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STARK – some resultsSTARK – some results
Elasped time
58
55
52
49
46
43
40
37
34
31
28
25
22
19
16
13
10
7
4
1
Co
nst
rain
t vi
ola
tion
s
100
90
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50
40
30
STARK 1
STARK 2
STARK 3
CON 1
CON 2
CON 3
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HuSSHHuSSH• Allowing users to create their own
heuristics “on the fly”• Capturing and reusing successful
heuristic approaches allows the decision maker to work at a higher level
• User empowerment and satisfaction is raised by these approaches
• Users can learn to use sophisticated tools
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HuSSH sample resultHuSSH sample result
730
740
750
760
770
780
790
800
810
10 20 30 40 50 60 70 80 90
Time (%)
No.
Exa
ms
0
50
100
150
200
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450
500
Pen
alty
ExamsPenalty
í
u- Unsched-Sched.
m Manual
mm m u-s u-s m Fig. 2b
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Dynamic Scheduling - Dynamic Scheduling - steelsteel
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Using AgentsUsing Agents`
User agent
HSM AgentSY Agent
CC-1 Agent CC-3 AgentCC-2 Agent
user
Continuous Casters Slabs
Hot Strip MillSlabyard
coils
Ladle
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Stability, Utility and Stability, Utility and RobustnessRobustness
Utility ( Sstatic, Sdynamic, E, t) = F dynamic - Fstatic
Robustness (S)= R .Utility - (1-R).Stability,where R is a real valued weight in the range [0,1].
E is the real-time event.
N
i iidynamicstatic CCtESSStability1
'),,,(
Utility ( Sstatic, Sdynamic, E, t) = F dynamic - Fstatic
Robustness (S)= R .Utility - (1-R).Stability,where R is a real valued weight in the range [0,1].
E is the real-time event.
N
i iidynamicstatic CCtESSStability1
'),,,(
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Remaining Scheduled coils
Delete the non-available coils
Unscheduled coils
Reoptimise considering the unscheduled coils
Processed coils
Schedule RepairSchedule Repair
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Simulation PrototypeSimulation Prototype
Prototype Developed for Simulation
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Some ResultsSome Results
0
100
200
300
400
500
600
700
-700 -600 -500 -400 -300 -200 -100 0
Utility
Stability
NOT SR CSR OSR HCSR HOSR PR CR
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Case studiesCase studies
• SORTED – Nationwide building society
• SteelPlanner – A.I. Systems BV• Inventory Management – Meads• Workforce Scheduling - BT• Electronics Assembly - Mion• Nurse rostering – several Belgian
Hospitals
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Conclusion – Open Conclusion – Open ProblemsProblems
• Optimisation can improve productivity• Optimisation can be made easier to use
and more applicable• Needed:
– Robust, widely applicable optimisation algorithms/heuristics
– Modelling languages and software toolboxes– Champions and consultants– Better understanding of human problem
solving for use in HCI– Higher levels of computer use and literacy