CAREER: Deterministic Approximations in Rolling Horizon Procedures under Forecast Uncertainty

9701403 Ryan这个CAREER奖的研究部分涉及顺序决策领域的几个重要主题。决策者经常面临着在可能无限的范围内对系统做出一系列决策的问题，而环境条件的固有不确定性会影响系统状态。标准的多时期决策技术在将未来时期的预测信息与当前时期的决策相结合方面往往是不足的。本研究的重点是将一种通用而灵活的预测方法与iq建模为马尔可夫决策过程的滚动地平线过程相结合。研究目标是：(1)确定有限确定性子问题的适当长度和公式，以考虑预测的不确定性；(2)评估每个近似级别对决策长期成本的影响；(3)衡量预测修正的影响。拟议的教育活动旨在通过采用积极和合作的学习策略，提高学生在本科阶段的理解和记忆力，并通过参与教学同行审查项目，提高教学效率。在许多不同的情况下，在不确定的长期（或无限）计划项目中使用顺序决策。例如，电力公司必须在无限的范围内向客户提供电力，随着时间的推移，电力需求存在不确定性。制造企业必须根据不确定的需求估计，为产能扩张分配资金。这项研究应该会对我们在长期规划中更好地做出决策的能力产生影响。此外，教育部分应该对工程基础设施产生积极影响，鼓励本科生的跨学科和团队思维。

9701403 Ryan The research component of this CAREER award addresses several important topics in the area of sequential decision making. Decision makers are often faced with making a sequence of decisions about a system over a possibly infinite horizon, with inherent uncertainty in the environmental conditions influencing the system state. Standard multiperiod decision-making techniques are often inadequate in integrating forecast information for future time periods with the decision made in the current time period. This research is focused on integrating a general and flexible forecasting approach with a rolling horizon procedure that iq modeled as a Markov decision process. The research objectives are to: (1) determine appropriate lengths and formulations of the finite deterministic subproblems to account for forecast uncertainty, (2) assess the effect of each level of approximation on the long term cost of the decisions, and (3) gauge the effect of forecast revisions. The proposed educational activities are intended to improve student understanding and retention at the undergraduate level through the use of active and cooperative learning strategies, and enhance teaching effectiveness by participation in a project on the peer review of teaching. The use of sequential decision-making for long- (or infinite-) term planning projects under uncertainty arise in a number of different contexts. For example, electric utilities must deliver power to customers over an infinite horizon, with uncertainty in the demand for electricity over time. Manufacturing concerns must allocate funds for capacity expansion over time based on uncertain demand estimates. This research should have an impact on our ability to better make decisions over long planning horizons. Moreover, the educational component should have a positive impact on the engineering infrastructure, encouraging interdisciplinary and team-thinking among undergraduate students.