Queueing Systems with Server Synchronization

该项目涉及通过开发更好的劳动力管理技术来改善业务运作。 我们的目标是确定工人应该如何动态分配任务的存在不确定的需求和处理要求的各种产品和服务。 主要的新奇是将同步约束的工人分配。 例如，某些任务的完成可能需要一组工人，而其他任务每次需要一个工人。 在这两种情况下，工人必须同步他们的时间表，因为他们必须同时在相同或不同的任务。 该项目涉及到开发易于实施，强大的，动态的政策，分配工人的任务在真实的时间，考虑到这种同步的工人分配的限制。 潜在的应用领域包括医疗保健交付、车间调度、呼叫中心、项目管理和并行计算。 如果获得成功，该奖项将产生有价值的见解和具体的指导方针，如何通过更好地利用现有的工人和其他资源，以低成本提高一大类生产和服务系统的性能。计划中的研究包括将企业建模为网络，将工人建模为服务器。 研究目标将通过样本路径分析，马尔可夫决策过程，流体极限分析，计算机模拟和线性规划来实现。特别是，对于具有一般拓扑结构和无限缓冲区的网络，将使用流体极限分析来确定服务器在各种可能的服务器配置中应该花费的时间比例。 然后，动态服务器分配政策，来任意接近是最优的将被开发。另一方面，有限缓冲区的系统将使用马尔可夫决策过程技术进行分析，其中将有服务器分配的约束。 所提出的研究涉及当不同服务器的调度必须同步时（例如，以说明所需的协作或非协作）。 重点是最大限度地提高系统容量（吞吐量），但也将考虑其他性能指标，包括持有成本。

This project is concerned with improving business operations through the development of enhanced workforce management techniques. The goal is to determine how workers should be assigned dynamically to tasks in the presence of uncertain demand and processing requirements for various products and services. The main novelty is the incorporation of synchronization constraints on worker assignment. For example, the completion of certain tasks may require a team of workers, whereas other tasks require one worker at a time. In both cases, workers must synchronize their schedules in that they must be simultaneously at either the same or different tasks. This project involves the development of easily implementable, robust, and dynamic policies for assigning workers to tasks in real time, taking into account such synchronization constraints on worker assignment. Potential application areas include health care delivery, job shop scheduling, call centers, project management, and parallel computing. If successful, this award will yield valuable insights and concrete guidelines for how the performance of a large class of production and service systems can be improved at low cost by better utilizing the available workers and other resources.The planned research involves modeling businesses as queueing networks and workers as servers. The research objectives will be achieved through sample-path analysis, Markov decision processes, fluid limit analysis, computer simulation, and linear programming. In particular, for networks with general topologies and infinite buffers, fluid limit analysis will be used to identify what fraction of time the servers should spend in the various possible server configurations. Then dynamic server allocation policies that come arbitrarily close to being optimal will be developed. On the other hand, systems with finite buffers will be analyzed using Markov decision process techniques, where there will be constraints on server assignments. The proposed research involves the rigorous analysis and control of queueing systems with flexible, heterogeneous servers when the schedules of different servers must be synchronized (e.g., to account for needed collaboration or non-collaboration). The focus is on maximizing system capacity (throughput), but other performance measures will also be considered, including holding costs.