CAREER: The Effects of Centralized and Decentralized Sequential Decisions on System Performance

职业：集中式和分散式顺序决策对系统性能的影响

• 批准号: 1553274
• 负责人: Alessandro Arlotto
• 金额: $ 50万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1553274&HistoricalAwards=false
• 关键词: CAREER; Effects; Centralized; Decentralized; Sequential

The goal of this Faculty Early Career Development (CAREER) grant is to develop a comprehensive understanding of the immediate and long-term effects of centralized and decentralized sequential decisions. Individuals, organizations and firms make decisions under uncertainty on a regular basis. Such decisions are often complex and difficult to make, and their implications are difficult to measure. This research will develop approaches that quantify the uncertainty arising during the decision-making process. In addition, this research will also address decisions that involve sequential routing and scheduling and require the completion of multiple tasks in an interrelated network. The findings will highlight the effects of multiple sequential decisions that go beyond (nearly-) optimizing different complex systems. Such sequential decision models provide a natural framework for the analysis of inventory control, queueing, revenue management, routing, and many other problems frequently arising in practice. Therefore, this research will provide new fundamental theoretical results for academics as well as key insights for practitioners. Furthermore, this award will foster the education and the development of undergraduate and graduate students who will take an active part in several research projects. This award will also help expanding the graduate curriculum through the development of new Ph.D. courses that integrate the results of this research. In all the activities, the principal investigator seeks to promote the participation of underrepresented groups in the sciences and engineering.The award will support two complementary lines of work. The first considers centralized decisions. It is motivated by the a priori possibility that the realized rewards may often be far away from their optimal expected value, and it provides a detailed probabilistic analysis of dynamic programming policies. Specific topics include limit theorems for controlled transient systems (including knapsack problems), distributional equivalences across formulations with different horizon lengths, and the identification of policies that lead to the same limiting distribution as the one obtained by implementing the optimal policy. The second line of work studies decentralized decisions. It tackles the complexity that comes from combining a network structure with the routing of self-interested parties who require multiple different services. As a result, this research will characterize equilibrium routing behaviors in queuing networks, compare the performance of a system with self-interested agents with the performance of the same system when managed by a central planner, and design mechanisms that aim to align the centralized and the decentralized solutions.

这项教师早期职业发展（Career）资助的目标是培养对集中和分散顺序决策的即时和长期影响的全面理解。个人、组织和公司经常在不确定性的基础上做出决策。这样的决定往往是复杂而难以做出的，其影响也难以衡量。本研究将发展量化决策过程中产生的不确定性的方法。此外，本研究还将解决涉及顺序路由和调度的决策，并要求在相互关联的网络中完成多个任务。这些发现将突出多个顺序决策的影响，这些决策超越了（几乎）优化不同的复杂系统。这样的顺序决策模型为库存控制、排队、收益管理、路由和许多其他在实践中经常出现的问题的分析提供了一个自然的框架。因此，本研究将为学术界提供新的基础理论成果，并为实践者提供关键见解。此外，该奖项将促进本科生和研究生的教育和发展，他们将积极参与几个研究项目。该奖项还将通过开发整合本研究成果的新博士课程来帮助扩展研究生课程。在所有的活动中，首席研究员寻求促进在科学和工程中代表性不足的群体的参与。该奖项将支持两个互补的工作方向。第一个考虑集中决策。它的动机是实现的回报可能经常远离其最优期望值的先验可能性，并提供了动态规划策略的详细概率分析。具体的主题包括控制暂态系统的极限定理（包括背包问题），不同视界长度的公式之间的分布等价，以及导致与实现最优策略获得的相同极限分布的策略的识别。第二部分研究分散决策。它解决了将网络结构与需要多种不同服务的自利益方的路由相结合所带来的复杂性。因此，本研究将描述排队网络中的均衡路由行为，比较具有自利益代理的系统性能与由中央计划器管理的相同系统的性能，并设计旨在使集中和分散解决方案保持一致的机制。