Data-driven Dynamic Scheduling

在动态调度问题中，目标是最优地分配到达的作业（例如，制造业中的产品订单;医疗保健中的患者）到资源（例如，制造业中的机器;医疗保健中的护士）在持续变化的环境中随着时间的推移（例如，机器坏了;新的更紧急的病人来了）。 虽然已经提出了许多调度优化方法，并在调度软件中实现，但实际上，最终用户不一定能接受所得到的最优调度。通常，在实现之前手动修改调度，并且如果调度反复不满足用户期望，则软件本身可能被丢弃。该提案的主要前提是，系统的真实特征和用户偏好由收集的关于问题及其过去（实施）解决方案的数据捕获，因此，软件创建的时间表与用户期望的时间表之间的不匹配可以通过数据驱动的建模来减少或消除。 现代信息系统能够收集关于过去作业和资源特性的数据（例如，到达时间、处理时间），实时系统状态更新（例如，当前在系统中的作业的数量），以及过去的调度决策（例如，过去管理者的决策）。传统上，这些数据源中的每一个都被认为是孤立的不同的研究领域，即调度理论，经典调度和逆调度，分别。 我的研究计划将利用历史和实时系统和偏好数据的可用性，通过组合调度，优化理论和逆优化技术的集成。由此产生的混合调度模型将捕捉系统的特点和用户的喜好比传统的方法，产生的时间表，将更容易被用户接受。因此，这项研究有可能增加调度软件在实践中的采用，这反过来又会导致这些系统的生产力和效率更高。

In dynamic scheduling problems, the aim is to optimally assign arriving jobs (e.g., product orders in manufacturing; patients in healthcare) to resources (e.g., machines in manufacturing; nurses in healthcare) over time in a continuously-changing environment (e.g., machines break down; new more urgent patients arrive). While many optimization methods for scheduling have been proposed and implemented in scheduling software, in practice the resulting optimal' schedules are not necessarily acceptable to the end users. Frequently, the schedules are manually modified before implementation, and, if the schedules repeatedly do not meet user expectations, the software itself might be discarded. The main premise of this proposal is that the true characteristics of the system and user preferences are captured by the data collected about the problem and its past (implemented) solutions, and that as a result the mismatch between the schedules created by software and those desired by the user can be reduced or eliminated through data-driven modelling. Modern information systems are capable of collecting data on past job and resource characteristics (e.g., arrival times, processing times), real-time system status updates (e.g., number of jobs currently in the system), and past scheduling decisions (e.g., decisions made by managers in the past). Traditionally, each of these data sources has been considered in isolation by distinct research areas, namely queueing theory, classical scheduling, and inverse scheduling, respectively. My research program will leverage the availability of both historical and real-time system and preference data through the integration of combinatorial scheduling, queueing theory and inverse optimization techniques. The resulting hybrid scheduling models will capture system characteristics and user preferences better than traditional approaches, producing schedules that would be more readily accepted by the users. Thus, this research has the potential to increase the adoption of scheduling software in practice, which will in turn lead to greater productivity and efficiency of those systems.