The aim of this study was to develop a computational account of the spontaneous task ordering that occurs within jobs as work unfolds ("on-the-fly task scheduling").

Air traffic control is an example of work in which operators have to schedule their tasks as a partially predictable work flow emerges. To date, little attention has been paid to such on-the-fly scheduling situations.

We present a series of discrete-event models fit to conflict resolution decision data collected from experienced controllers operating in a high-fidelity simulation.

Our simulations reveal air traffic controllers’ scheduling decisions as examples of the partial-order planning approach of Hayes-Roth and Hayes-Roth. The most successful model uses opportunistic first-come-first-served scheduling to select tasks from a queue. Tasks with short deadlines are executed immediately. Tasks with long deadlines are evaluated to assess whether they need to be executed immediately or deferred.

On-the-fly task scheduling is computationally tractable despite its surface complexity and understandable as an example of both the partial-order planning strategy and the dynamic-value approach to prioritization.