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Understanding Task Behavior: Optimization With Point of Saturation Prediction in Multiple Locations

Analyzing the rise and fall of planned tasks using exponential curve prediction in 2012. Determining optimum work timelines, considering Normal Probability Distribution for portal selection and performance evaluation. Plan work to end at saturation point for optimal results.

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Understanding Task Behavior: Optimization With Point of Saturation Prediction in Multiple Locations

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  1. Rising exponential Falling exponential 2012. Planed tasks Prediction of behavior for each task! We should determine the time constant τ

  2. We assume exp. Curve scenario Math and portals

  3. STOP WORK HERE 37% OF WORK LEFT (TO BE DONE) 2012.

  4. We stop work when curve starts to go in saturation! When to stop work!

  5. Large number of portal with different Locations ... ...end up with different results

  6. Portals behave similar ... ... We select best portals !!!

  7. There are differences • Depends if portal is already live? • If work will be continued after planed period • etc....

  8. The Normal Probability Distribution f (x ) • Graph of the Normal Probability Density Function x 

  9. What we will have?

  10. 10% of portals will be extraordinary!!!

  11. Normal Curve Approximate percentage of area within given standard deviations (empirical rule). 99.7% 95% 68%

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