Hum Factors. 2014 Sep;56(6):1093-112. doi: 10.1177/0018720814525629.
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.
本研究旨在为工作中出现的自发任务排序（“实时任务调度”）提供一种计算方法。
空中交通管制是一种需要实时调度任务的工作，因为工作流程是部分可预测的。迄今为止，对这种实时调度情况的关注较少。
我们提出了一系列离散事件模型，这些模型适用于从在高保真模拟中操作的经验丰富的控制器收集的冲突解决决策数据。
我们的模拟结果揭示了空中交通管制员的调度决策，这些决策是 Hayes-Roth 和 Hayes-Roth 的部分有序规划方法的示例。最成功的模型使用机会主义的先来先服务调度从队列中选择任务。具有短截止日期的任务将立即执行。对具有长截止日期的任务进行评估，以确定是否需要立即执行或推迟执行。
尽管表面上很复杂，但实时任务调度在计算上是可行的，并且可以理解为部分有序规划策略和优先级的动态值方法的示例。
