Personnel rostering in operational settings -- e.g., air, road, rail and sea transportation, first responders (police, fire, and rescue), medicine, power generation, resource extraction, and military operations – has, thus, far centered on the logistics of resources and availability of employees, largely ignoring the performance capability of the employees as driven by their sleep/wake biology. Thus, rosters involving shift work and periods of extended wakefulness often cause individuals to work at times of increased sleepiness and reduced alertness and performance due to sleep loss and unfavorable timing on the endogenous biological clock; and conversely to attempt sleep when sleep is compromised by pressure for wakefulness from the endogenous biological clock. This puts organizations and individuals at risk for poor performance, increased errors, diminished productivity, accidents, and reduced safety.
Recent developments in biomathematical modeling of performance on the basis of sleep/wakefulness have made it possible to predict times of increased sleepiness in groups of workers. This has made it feasible to optimize sleep/wake/work schedules not only on the basis of resource logistics, but also on the basis of sleep/wake-based performance prediction.
WSU inventors have developed a system and procedure for rostering and scheduling to reduce fatigue and its consequences by integrating mathematical models capable of predicting fatigue with software/hardware components capable of optimizing rosters and/or work schedules. As a result, rosters/schedules that are conducive to good performance while meeting operational demands for personnel and complying with applicable regulations can be produced. The resulting rosters and/or work schedules can help to sustain performance, productivity, safety, and well-being, while reducing errors, incidents, accidents, and attendant human and economic losses.
Applications and Advantages
§ Provides a modern approach for rostering and optimizing work schedules;
§ Offers a fatigue model that is adjustable to predict objectively measurable loss in productivity (e.g., in transportation--increased fuel consumption and increased maintenance) and other operationally relevant performance outcomes.