Ubiquitous Optimisation Making Optimisation Easier to Use Prof Peter Cowling

Optimisation in Decision Making Uncontrollable factors DesirabilityDesirabilityDesirabilityDesirability Current situation D1 D2 D3 D4 Controllable factors Outcomes

Modelling Ill-structured Complex Abstract Well-structured Simple Concrete Model Conceptual Model Tangible system Creation Testing Reflection Extraction

Optimisation Evolutionary Algorithms Artificial Intelligence Operational Research Novel Ideas

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?

Beneficiaries 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 ubiquitous optimisation closer Speech/gesture input/output Intelligent, learning computers Cognitive science advances Ambient computing Control/sensor technologies Increased IT awareness among managers/engineers

Angles 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

Hyperheuristics L.L. Heuristic performance Hyperheuristic Heuristic Choice Low level heuristics Problem Solution quality Solution perturbation

Benefits 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 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 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 applications Timetabling mobile trainers Nurse rostering Scheduling project meetings Examination timetabling

Other 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 Interaction

STARK diagrams

Representing constraints Room capacity violation Period limit violation

STARK – some results

HuSSH 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 result

Dynamic Scheduling - steel

Using Agents ` User agent HSM Agent SY Agent CC-1 Agent CC-3 Agent CC-2 Agent user Continuous Casters Slabs Hot Strip Mill Slabyard coils Ladle

Stability, Utility and Robustness

Remaining Scheduled coils Delete the non-available coils Unscheduled coils Reoptimise considering the unscheduled coils Processed coils Schedule Repair

Simulation Prototype

Some Results

Case 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 Problems 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