A multilevel methodology for supply chain network design

该研究项目旨在为设计和规划更高效、更具弹性和更可持续的供应链做出贡献。这项提议的重点是开发先进的工具和规划系统，以支持和加强供应链中的战略规划。支持这项研究的方法是基于最优化、分布式决策和多代理系统。设计供应链(SCND)涉及许多相互关联的决策，如选择其生产-分销设施的数量、位置、任务分配、能力和技术，选择关键供应商，以及经常确定公司应该服务于许多潜在产品市场中的哪个市场，以及应该向该市场提供什么产品。这一问题具有重大挑战，但也提供了显著的好处：在许多行业背景下，更好的供应链结构会带来更低的物流成本、更好的客户服务和更短的交货期，从而使公司能够增加市场份额和盈利能力。独立研究估计，通过重新设计供应链，供应链成本的总降幅在5%至15%之间。SCND涉及两个主要活动：生成候选设计和评估这些设计。最近的调查表明，尽管有大量关于这个主题的文献，但除了场景规划之外，关于设计评估的工作非常有限。拟议的研究将开发新的方法来估计供应链设计在从正常条件到严重中断的各种操作环境下的真实世界性能。这反过来将允许改进设计过程本身。

This research programs aims to contribute to the effort of designing and planning more efficient, resilient and sustainable supply chains. The focus of this proposal is on developing advanced tools and planning systems to support and enhance strategic planning in supply chains. Methodologies supporting this research are based on optimization, distributed decision making, and multi-agent systems. Designing a supply chain (SCND) involves many interrelated decisions, such as selecting the number, location, mission assignment, capacities and technologies of its production-distribution facilities, selecting the key suppliers, and often determining which of the many potential product-markets the company should serve and what offer should it make to that market. This problem has significant challenges, but also provides significant benefits: in many industrial contexts, a better supply chain structure leads to lower logistics costs, better customer service and shorter lead times, allowing the company to increase its market share and profitability. Independent studies have estimated the total reduction of supply chain costs from redesigning a supply chain to be in the 5 to 15% range.  SCND involves two main activities: generation of candidate designs and the evaluation of those designs. More recent surveys show that despite the vast literature on the topic, there is very limited work on design evaluation beyond scenario planning. The proposed research will develop new methodologies to estimate the real-world performance of supply chain designs under varying operational contexts ranging from normal conditions to severe disruptions. This will in turn allow to improve the design process itself.