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;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的未来需求的信息很少;竞争性项目调度问题，模拟企业之间在新产品开发中的竞争;用于选择可能遭受随机故障的服务设施的最佳位置的模型，或者制造商和配送中心的位置和补充参数，这些制造商和配送中心向面临随机需求的给定零售商集合供应。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，以便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; online supply chain scheduling problems that help coordinate scheduling, batching, and delivery decisions in supply chains where little information about future demand is available; competitive project scheduling problems that model competition between firms in development of new products; models for choosing optimally locations of service facilities that may be subject to random failures, or locations and replenishment parameters of manufacturers and distribution centers that supply a given set of retailers facing random demand. 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 allow 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.