Nonclassical Discrete Optimization Problems

在这个研究项目中，我们将研究在复杂的制造和服务系统中的计划操作中出现的几类离散优化模型。这些类包括网络建设问题，处理规划建设活动，以建立新的运输网络或恢复受损的网络;鲁棒优化模型，处理决策下的不确定性;半在线供应链调度问题，帮助协调调度，订货和交付决策的供应链中，只有部分信息的未来需求是可用的;模型选择最佳位置的服务设施，可能会受到随机故障。这些模型是“非经典”的，因为它们是新的或最近出现在文献中，目前还没有足够有效的计算方法来处理它们。该研究计划的主要目标是开发一套理论结果和计算方法，使其能够在实践中使用这些模型，并了解其可能性和局限性。这将提供新的强大的工具，从业者谁处理规划复杂的行动，带来新的理解该地区，并打开新的重要的研究方向。

In this research program, we will study several classes of discrete optimization models that arise in planning operations in complex manufacturing and service systems. These classes include network construction problems that deal with planning construction activities for building new transportation networks or restoring damaged networks; robust optimization models that deal with decision-making under uncertainty; semi-online supply chain scheduling problems that help coordinate scheduling, batching, and delivery decisions in supply chains where only partial information about future demand is available; models for choosing optimally locations of service facilities that may be subject to random failures. The models are "non-classical" in the sense that they are new or appeared in the literature recently, and at present there are no sufficiently effective computational approaches to handle them. The main goal of this research program is to develop a set of theoretical results and computational methods that will make it possible to use these models in practice and to understand their possibilities and limitations. This will provide new powerful tools to practitioners who deal with planning complex operations, bring new understanding of the area, and open new important research directions.