Supply chain optimization under uncertainty

供应链计划系统中的许多挑战源于几个不确定因素的存在，例如需求、交货期和服务和运营成本。未能考虑到这些不确定性可能导致服务水平降低、需求损失以及从计划外结果中恢复所需的大量额外费用。我们的研究重点是为供应链系统设计决策算法，该算法能够包含从历史数据中提取的不确定性，并且可以利用数据分析来帮助计划者做出风险知情的决策并对不确定性做出主动反应。第一个研究主题集中在分销网络设计和库存优化的整合，这是供应链战略和战术规划中的两个关键组成部分，具有很强的互动性。这一研究主题涉及不确定性下的优化的多种解决方案方法，以及数据挖掘技术的增强。第二个研究主题涉及随机组合优化算法的发展，这是运筹学中一个非常具有挑战性的问题，可以应用于几个物流和运输应用，如车辆路径。第三个研究主题涉及动态生产和库存路线的算法设计，可用于处理以动态方式显示需求等重要投入的情况，以及当运营环境或投入立即发生变化时，因此必须迅速确定修订的解决方案。我们的目标是1)提供有效的跨学科解决方案框架来处理这些具有挑战性的问题，2)同时专注于可导致该领域新研究发展的实际和创新应用，以及3)为供应链从业者提供跨不同解决方案方法的见解和影响。我们计划与工业合作伙伴一起开展一些项目，他们将为科学发展提供测试实例和实用观点。

Many of the challenges in supply chain planning systems stem from the presence of several sources of uncertainty such as demand, lead time and costs of services and operations to name a few. Failure to account for these uncertainties can result in a reduced service level, loss of demand and significant extra expenses to recover from unplanned outcomes. Our research focuses on the design of decision algorithms for supply chain systems that can incorporate uncertainty which can be extracted from historical data and can make use of data analytics in order to help the planners make risk informed decisions and proactively react to the uncertainty. The first research theme focuses on the integration of distribution network design and inventory optimization, two critical components in the supply chain strategic and tactical planning that have strong interactions. This research theme involves multiple solution methodologies for optimization under uncertainty together with enhancements from data mining techniques. The second research theme concerns the development of solution algorithms for stochastic combinatorial optimization, a very challenging problem in operations research, which can be applied to several logistics and transportation applications such as vehicle routing. The third research theme involves algorithmic designs for the dynamic production and inventory routing that can be used to deal with situations where important inputs such as demand is revealed in a dynamic fashion, as well as when there are immediate changes in the operational circumstances or inputs, and thus a revised solution must be quickly determined. We aim to 1) provide efficient interdisciplinary solution frameworks to deal with these challenging issues, 2) while focusing on practical and innovative applications that can lead to new research developments in the fields and yet 3) providing insights and implications across different solution methodologies for supply chain practitioners. We plan to work on some of the projects with industrial partners who will provide test instances and practical points of view for the scientific developments.