Is your crew planning circadian aware?

The circadian regulation of sleep and wakefulness is a fundamental mechanism in human physiology. Together with time awake and prior sleep debt, the circadian rhythm explains much of the variation in cognitive performance seen in response to work/rest schedules. Because this mechanism is so robust, correctly identifying and leveraging its timing is critically important in managing fatigue risk.

Despite this, most operators assume that all crew members fall into an “intermediate” chronotype—those whose optimal sleep period is centered around 3 a.m., such as an 11 p.m.–7 a.m. sleep schedule. However, there is significant variability in chronotypes. Some crew members are inclined toward morningness (“larks”), while others lean toward eveningness (“owls”). But what if this information could be harnessed during crew planning? How much of a difference could it make?

We set out to explore this question by simulating work schedules based on full knowledge of crew chronotypes (often referred to as “diurnal types”) and comparing it to the traditional assumption that all crew are intermediate types. We distributed crew across the spectrum of chronotypes, from larks to owls, and ran the numbers using a large short haul planning problem. The result? Fatigue risk was reduced by an additional 29% when individual chronotype information was incorporated. That’s a significant reduction!

Of course, it’s not realistic to expect every crew member to willingly share their chronotype with the company. A strategy could look like this to improve planning accuracy:

1. Use chronotype data where available: If chronotype information is voluntarily provided, make sure to use it in your BMM,

2. Leverage stated preferences: Crew preferences for morning or evening shifts can serve as useful proxies for chronotype.

3. Use age as a proxy: Chronotypes tend to shift with age, with younger individuals often leaning toward eveningness and older individuals transitioning to morningness.

By employing this strategy, fatigue models can better align with crew-specific circadian rhythms, leading to improved predictions and thereby, depending on the problem, significantly reduce risk.

One tool that enables this level of customization is our bio-mathematical model, BAM. Highly configurable, BAM allows individual inputs to be incorporated while interacting with industry-strength optimizers in real time to design crew work patterns (pairings and rosters). Compare BAM to other models here.