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Ubiquitous Optimisation

Ubiquitous Optimisation

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Ubiquitous Optimisation

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  1. Ubiquitous Optimisation Making Optimisation Easier to Use Prof Peter Cowling http://www.mosaic.brad.ac.uk

  2. Optimisation in Decision Making Outcomes Uncontrollable factors Desirability Current situation D4 D3 D2 D1 Controllable factors

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

  4. NP-hard Optimisation Operational Research Evolutionary Algorithms Novel Ideas Artificial Intelligence

  5. 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

  6. Ubiquitous?

  7. 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.

  8. 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

  9. 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

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

  11. 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

  12. 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

  13. 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

  14. Other applications • Timetabling mobile trainers • Nurse rostering • Scheduling project meetings • Examination timetabling

  15. 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

  16. Human-Computer Interaction

  17. STARK diagrams

  18. Representing constraints Room capacity violation Period limit violation

  19. STARK – some results

  20. 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

  21. HuSSH sample result

  22. Dynamic Scheduling - steel

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

  24. Stability, Utility and Robustness

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

  26. Simulation Prototype

  27. Some Results

  28. 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

  29. 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