Collaborative Proposal: Resource Allocation with Learning in Dynamic and Partially Observable Networks

这个合作项目将研究在不确定和学习的情况下，相互依赖的道路和服务网络的连通性恢复。实际应用包括灾后碎片清除，以支持灾害应对活动，以及在中断后恢复和修复相互依赖的基础设施。例如，当公路网(部分)中断时，需要恢复边缘(道路)(例如，通过修复和清除碎片)。目标是在供应和需求节点之间及时建立连接，以满足需求。在不确定的情况下(关于网络条件和供需水平)，可以通过收集态势空间数据来改进决策。然而，数据收集会耗费时间和资源。此外，一组公共资源可以执行恢复和学习活动。因此，在资源和时间限制的情况下，动态确定边缘恢复的优先顺序以及恢复和学习之间的资源分配对于操作效率和效果至关重要。该项目考虑了学习和恢复活动之间的权衡，以及(公平和及时的)资源分配和信息更新频率的决定。该项目将利用佐治亚理工学院卫生和人道主义系统中心建立的外联网络，在项目执行期间传播结果并与实践者互动。以前对具有学习的随机网络中的网络连接的研究有限。如果成功，该项目将引入网络修复模式，其特点是资源有限、不确定性、能够通过部署资源来减少不确定性，以及需要维护服务访问的公平性。它将有助于更深入地理解如何解决随机网络中的动态多阶段决策问题，因为随机网络提供了更新信息的机会，并且学习和恢复行动共享相同的资源。这项研究将导致寻找最优或接近最优解的有效方法。研究团队将对简单网络上的潜在问题进行结构性分析，评估不同的信息更新机制，并利用这些机制得出见解并生成定制的解决方案。

This collaborative project will research restoration of connectivity of interdependent road and service networks under uncertainty and learning. Real-life applications include post-disaster debris clearance to enable disaster response activities and interdependent infrastructure recovery and repair after a disruption. For example, when the road network is (partially) disrupted, edges (roads) need to be restored (e.g., through repair and debris clearance). The goal is to establish connectivity between supply and demand nodes in a timely manner to satisfy demand. Under uncertainty (about network conditions, and supply-demand levels), decision-making can be improved by collecting situational spatial data. However, data collection consumes time and resources. Furthermore, a common set of resources may perform both restoration and learning activities. Hence, under resource and time constraints, decisions on dynamically prioritizing edge recovery and resource allocation between recovery and learning are crucial for operational efficiency and effectiveness. This project considers the trade-off between learning and restoration activities, and decisions on (equitable and timely) resource allocation and frequency of information updates. The project will leverage the outreach network established by the Center for Health and Humanitarian Systems at Georgia Tech to disseminate results and interact with practitioners during project execution.Previous research on network connectivity in stochastic networks with learning is limited. If successful, this project will introduce network repair models that are characterized by limited resources, uncertainty, ability to reduce uncertainty by deploying resources, and the need to maintain fairness in service access. It will contribute to a deeper understanding of how to solve dynamic multi-stage decision problems in stochastic networks that offer an opportunity to update information and in which learning and recovery actions share the same resources. This research will lead to efficient solution approaches for finding optimal or near-optimal solutions. The research team will perform structural analysis of underlying problems on simple networks, evaluate different information update mechanisms, and use those to derive insights and generate customized solutions.