Risk Management in Supply Chain Design and Operations

这项研究将解决与供应链风险管理相关的问题。风险包括中断(自然灾害、劳资纠纷、供应商业务失败和恐怖主义)、延误、系统风险、预测风险、知识产权风险、采购风险、库存风险和产能风险。风险缓解措施往往会导致规划/预测成本、持续成本或风险恢复成本的增加。该研究项目将开发基于优化的多目标模型，以支持供应链中的风险缓解规划。一个重要的重点领域将是不同风险和缓解战略之间的相互作用。减轻一种风险可能会增加对另一种风险的暴露。该项目还将研究在不显著增加成本的情况下最好地解决组合风险的缓解战略。如果成功，该项目将增强公司或政府机构在面对多重风险时确定供应链风险缓解战略的能力。该分类法将使规划者能够识别和分类各种风险。它还将为规划者提供广泛适用于所面临的主要风险的风险缓解方法的初始菜单。优化模型(S)将有助于探索降低风险的具体战略和计划，考虑到行业和政府供应链规划者面临的广泛目标。拟议的工作还将增强我们在面对不确定输入和条件时解决复杂多目标优化问题的能力。

This research will address issues related to risk management in supply chains. Risks include disruptions (natural disasters, labor disputes, supplier business failures, and terrorism), delays, system risks, forecasting risks, intellectual property risks, procurement risks, inventory risks and capacity risks. Risk mitigation measures often result in increases in planning/forecasting costs, on-going costs, or risk recovery costs. The research project will develop optimization-based multi-objective models to support risk mitigation planning in supply chains. An important focus area will be the interaction between different risks and mitigation strategies. Mitigating one risk may increase the exposure to another risk. The project will also examine mitigation strategies that best address a combination of risks without significantly adding to costs.If successful, this project will enhance the ability of a firm or government agency to determine a supply chain risk mitigation strategy in the face of multiple risks. The taxonomy will enable planners to identify and categorize a broad variety of risks. It will also provide planners with an initial menu of risk mitigation approaches that are broadly applicable to the primary risks being faced. The optimization model(s) will facilitate the exploration of specific strategies and plans to mitigate risks accounting for the broad range of objectives faced by industrial and governmental supply chain planners. The proposed work will also enhance our ability to solve complex multi-objective optimization problems in the face of uncertain inputs and conditions.