短视策略用于具有衰减价值作业的非抢占调度
Myopic Policies for Non-Preemptive Scheduling of Jobs with Decaying Value
AI总结：
本文研究了具有时间衰减价值作业的非抢占调度问题，分析了三种启发式策略的性能，提供了理论保障和数值实验，为医疗调度等场景提供实用指导。
摘要：
在许多调度应用中，最小化延迟非常重要。延迟的一个不利影响是作业完成的奖励可能随时间衰减。确实，在医疗环境中，获取护理的延迟可能导致更差的结局，如死亡风险增加。受灾难场景下医院运营管理及其他易腐库存控制和信息服务应用的启发，我们考虑了具有时间衰减内部价值的作业的非抢占调度。由于求解最优调度策略计算上不可行，我们专注于三种直观启发式策略的性能：（1）最大化预期即时奖励的策略，（2）最大化预期即时奖励率的策略，（3）优先处理临近截止期限的作业的策略。我们为这三种策略提供了性能保障，并展示了许多性能界限是紧致的。此外，我们还提供了数值实验和模拟，以比较这些策略在各种场景中的表现。我们的理论和数值结果使我们能够为各种情况建立经验法则，包括患者调度场景。
英文摘要：
In many scheduling applications, minimizing delays is of high importance. One adverse effect of such delays is that the reward for completion of a job may decay over time. Indeed in healthcare settings, delays in access to care can result in worse outcomes, such as an increase in mortality risk. Motivated by managing hospital operations in disaster scenarios, as well as other applications in perishable inventory control and information services, we consider non-preemptive scheduling of jobs whose internal value decays over time. Because solving for the optimal scheduling policy is computationally intractable, we focus our attention on the performance of three intuitive heuristics: (1) a policy which maximizes the expected immediate reward, (2) a policy which maximizes the expected immediate reward rate, and (3) a policy which prioritizes jobs with imminent deadlines. We provide performance guarantees for all three policies and show that many of these performance bounds are tight. In addition, we provide numerical experiments and simulations to compare how the policies perform in a variety of scenarios. Our theoretical and numerical results allow us to establish rules-of-thumb for applying these heuristics in a variety of situations, including patient scheduling scenarios.
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