Ubiquitous OptimisationUbiquitous Optimisation

Making Optimisation Easier to Use

Prof Peter Cowling

http://www.mosaic.brad.ac.uk

Optimisation in Decision Optimisation in Decision MakingMaking

Uncontrollable factors

DDeessiirraabbiilliittyy

Current situation

D1

D2

D3

D4

Controllablefactors

Outcomes

ModellingModelling

•Ill-structured

•Complex

•Abstract

•Well-structured

•Simple

•Concrete

Model

Conceptual Model

Tangiblesystem

Creation

Testing

Reflection

Extraction

OptimisationOptimisation NP-NP-hardhard

Evolutionary Algorithms

Artificial Intelligence

Operational Research

Novel Ideas

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

Ubiquitous?Ubiquitous?

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.

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

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

HyperheuristicsHyperheuristics

L.L. Heuristic performance

HyperheuristicHeuristic

Choice

Low level heuristics

Problem

Solution quality

Solution perturbation

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

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

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

Other applicationsOther applications

• Timetabling mobile trainers• Nurse rostering• Scheduling project meetings• Examination timetabling

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

Human-Computer Human-Computer InteractionInteraction

STARK diagramsSTARK diagrams

Representing constraints Representing constraints Room capacity violation

Period limit violation

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

80

70

60

50

40

30

STARK 1

STARK 2

STARK 3

CON 1

CON 2

CON 3

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

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

250

300

350

400

450

500

Pen

alty

ExamsPenalty

í

u- Unsched-Sched.

m Manual

mm m u-s u-s m Fig. 2b

Dynamic Scheduling - Dynamic Scheduling - steelsteel

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

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

'),,,(

Remaining Scheduled coils

Delete the non-available coils

Unscheduled coils

Reoptimise considering the unscheduled coils

Processed coils

Schedule RepairSchedule Repair

Simulation PrototypeSimulation Prototype

Prototype Developed for Simulation

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

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

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