The coffee optimization algorithm

They heard you loud and clear. Here it is, suckers.

Caffeine dosing strategies to optimize alertness during sleep loss

No need anymore to frequent a Starfucks to get a jolt. Francisco G. Vital-Lopez,  Sridhar Ramakrishnan, Tracy J. Doty, Thomas J. Balkin and the one and only Jaques Reifman have been working hard.

It was not easy because "standard algorithms for solving MINLPs (i.e. branch-and-bound and simulated annealing) were not able to solve the optimization problem". We think you already knew this. Anyway.

You will certainly appreciate that the five dudes used "a validated mathematical model that accurately predicts the effects of sleep–wake schedules and caffeine consumption (i.e. the model inputs) on neurobehavioural (PVT) performance."

They did not stop there but went full bore into tabu search as you would expect. "In conjunction with a new implementation of the tabu search algorithm, the UMP allowed for the identification of near-optimal caffeine-dosing strategies in a practical computational time (i.e. in seconds)."

No more excuses. Maximize your neurobehavioural performance and avoid excessive caffeine consumption during any arbitrary sleep-loss condition.

Sleep loss, which affects about one-third of the US population, can severely impair physical and neurobehavioural performance. Although caffeine, the most widely used stimulant in the world, can mitigate these effects, currently there are no tools to guide the timing and amount of caffeine consumption to optimize its benefits. In this work, we provide an optimization algorithm, suited for mobile computing platforms, to determine when and how much caffeine to consume, so as to safely maximize neurobehavioural performance at the desired time of the day, under any sleeploss condition. The algorithm is based on our previously validated Unified Model of Performance, which predicts the effect of caffeine consumption on a psychomotor vigilance task. We assessed the algorithm by comparing the caffeine-dosing strategies (timing and amount) it identified with the dosing strategies used in four experimental studies, involving total and partial sleep loss. Through computer simulations, we showed that the algorithm yielded caffeine-dosing strategies that enhanced performance of the predicted psychomotor vigilance task by up to 64% while using the same total amount of caffeine as in the original studies. In addition, the algorithm identified strategies that resulted in equivalent performance to that in the experimental studies while reducing caffeine consumption by up to 65%. Our work provides the first quantitative caffeine optimization tool for designing effective strategies to maximize neurobehavioural performance and to avoid excessive caffeine consumption during any arbitrary sleep-loss condition.