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

协作提案：动态和部分可观察网络中的资源分配和学习

• 批准号: 1537824
• 负责人: Ozlem Ergun
• 金额: $ 16.13万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1537824&HistoricalAwards=false
• 关键词: Collaborative; Proposal; Resource; Allocation; Learning

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.

这个协作项目将研究在不确定性和学习下相互依存的道路和服务网络的连通性恢复。现实生活中的应用程序包括污点后碎片清除，以实现灾难响应活动和相互依存的基础设施恢复和维修。例如，当路线网络（部分）中断时，需要恢复边缘（道路）（例如，通过维修和碎屑清除）。目的是及时建立供应和需求节点之间的连通性，以满足需求。在不确定性（关于网络条件和供求水平）下，可以通过收集情境空间数据来改善决策。但是，数据收集会消耗时间和资源。此外，一组通用的资源可能同时进行恢复和学习活动。因此，在资源和时间限制下，关于动态优先考虑边缘恢复和恢复和学习之间资源分配的决策对于运营效率和有效性至关重要。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）资源分配和信息更新频率的决策。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展网络，以在项目执行期间传播结果并与从业人员进行交流。关于随机网络中学习网络连接性的研究是有限的。如果成功的话，该项目将介绍以有限的资源，不确定性，通过部署资源来减少不确定性的能力以及维持服务访问公平性的需求的网络维修模型。它将有助于更深入地了解如何在随机网络中解决动态的多阶段决策问题，这些决策问题提供了更新信息以及学习和恢复行动共享相同资源的机会。这项研究将导致有效的解决方案方法，以找到最佳或近乎最佳的解决方案。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，并使用这些方法来获得洞察力并生成自定义的解决方案。