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Ubiquitous Optimization: Enhancing Decision-Making Through Accessible Techniques

In the ever-evolving world of operational research and artificial intelligence, ubiquitous optimization represents a transformative approach to decision-making. Prof. Peter Cowling highlights the importance of making optimization easier to use, especially in complex and dynamic settings. He discusses practical applications such as scheduling, resource management, and time-tabling, demonstrating how novel algorithms, hyperheuristics, and intelligent systems can significantly enhance productivity across industries. By integrating human and artificial intelligence, we can empower managers and engineers to make informed decisions that yield substantial benefits.

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Ubiquitous Optimization: Enhancing Decision-Making Through Accessible Techniques

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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

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