主题：Process Flexibility: A Distribution-Free Approach to Long Chain Resilience

过程柔性：面向长链韧性的一种分布无关方法

时间: 2026年1月9日（周五）10:00

地点: 管理科研楼第二教室

主讲人: Li Chen

Bio: Li Chen is a Lecturer (Assistant Professor) in the Discipline of Business Analytics at The University of Sydney Business School. Prior to joining Sydney, he was a Research Fellow at the Institute of Operations Research and Analytics (IORA) at the National University of Singapore, where he received his PhD in Operations Research in 2022. He also holds a Bachelor of Science in Computational Mathematics from the University of Science and Technology of China, obtained in 2017. Li’s research focuses on the methodology and applications of optimization under uncertainty. His work has been published in leading operations and management science journals, including Operations Research, Production and Operations Management, and Transportation Research Part B: Methodological. His research has been recognized internationally, including being named Runner-up in the INFORMS Computing Society Harvey Greenberg Research Award (2023) and a Finalist in the INFORMS Transportation Science and Logistics Data-Driven Research Challenge (2025).

Abstract: Process flexibility has been a well-established supply chain strategy in both theory and practice for managing demand uncertainty. This study extends its application to mitigating supply disruptions by analyzing a long chain system. Specifically, we investigate the effectiveness of long chains in the face of random supply disruptions and demand uncertainty. We derive a closed-form, tight bound on the expected sales ratio of a long chain relative to full flexibility under random disruptions, thus providing a service-level guarantee. Our analysis shows that, when designed capacity equals expected demand, the fraction of benefits a long chain achieves relative to full flexibility increases with disruption probability; however, it decreases when capacity is instead expanded to match expected demand under disruptions. The long chain also demonstrates superior resilience, absorbing a significant portion of unexpected disruptions because of its sparsity. To generalize our findings, we introduce a moment decomposition approach that readily adapts to general piecewise polynomial performance metrics, maintaining tractability through a semidefinite program. This approach extends the traditional type II service metric (expected sales) to include a type I metric (probability of meeting full demand) and supports more flexible capacity–demand relationships. Applying this approach to the capacity configuration problem, we find that, without disruption, a long chain achieves target service levels with capacity comparable to full flexibility even with limited demand information. In contrast, disruptions significantly raise capacity requirements although long chains maintain a substantial advantage over dedicated systems. Our results highlight the resilience of long chains and the critical need to adapt capacity configuration decisions to supply disruption risks.