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

该合作项目将研究在不确定性和学习下相互依赖的道路和服务网络的连通性恢复。现实生活中的应用包括灾后废墟清理，以使灾害应对活动和相互依赖的基础设施恢复和修复后中断。例如，当道路网络（部分）中断时，需要恢复边缘（道路）（例如，通过修理和清除碎片）。目标是及时建立供需节点之间的连通性，以满足需求。在不确定性（关于网络条件和供需水平）的情况下，可以通过收集情景空间数据来改进决策。然而，数据收集消耗时间和资源。此外，公共资源集可以执行恢复和学习活动。因此，在资源和时间限制下，关于边缘恢复的动态优先级以及恢复和学习之间的资源分配的决策对于操作效率和有效性至关重要。该项目考虑学习和恢复活动之间的权衡，以及关于（公平和及时）资源分配和信息更新频率的决定。该项目将利用格鲁吉亚理工学院卫生与人道主义系统中心建立的外展网络来传播研究结果，并在项目执行期间与从业人员进行互动。如果成功的话，这个项目将引入网络修复模型，其特点是有限的资源，不确定性，通过部署资源来减少不确定性的能力，以及需要保持服务访问的公平性。它将有助于更深入地了解如何解决随机网络中的动态多阶段决策问题，这些问题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。这项研究将导致有效的解决方案的方法，找到最佳或接近最佳的解决方案。研究团队将对简单网络上的潜在问题进行结构分析，评估不同的信息更新机制，并利用这些机制来获得见解和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.