Supply chain optimization under uncertainty

不确定性下的供应链优化

• 批准号: RGPIN-2016-05822
• 负责人: Adulyasak, Yossiri
• 金额: $ 1.75万
• 依托单位: HEC Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709288
• 关键词: Supply; chain; optimization; under; uncertainty

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.

供应链计划系统中的许多挑战源于几个不确定性来源的存在，例如需求，交货时间以及服务和运营成本等。如果不考虑这些不确定性，可能会导致服务水平降低、需求减少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，这些算法可以结合从历史数据中提取的不确定性，并可以利用数据分析来帮助规划者做出风险明智的决策，并积极应对不确定性。第一个研究主题集中在分销网络设计和库存优化的集成，这两个关键组成部分在供应链战略和战术规划，有很强的相互作用。这个研究主题涉及多种解决方案的不确定性下的优化方法，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，一个非常具有挑战性的问题，在运筹学，它可以应用到几个物流和运输应用，如车辆路径的解决方案算法的发展。第三个研究主题涉及算法设计的动态生产和库存路由，可用于处理的情况下，重要的输入，如需求是在一个动态的方式，以及当有即时的操作环境或输入的变化，因此，必须迅速确定一个修订的解决方案。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）同时专注于实际和创新的应用，可以导致该领域的新研究发展，3）为供应链从业者提供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合作开展一些项目，他们将为科学发展提供测试实例和实践观点。