The Speakers

A supply chain network design problem (SCNDP) involves making long-term, irreversible strategic decisions whose cost efficiency depends on effectively leveraging flexibility and demand information. After analyzing the interaction of these factors, we propose five distinct policies for addressing the SCNDP. Starting with a localized production model where demand is satisfied locally, the study extends to scenarios where production capacity at one location serves other nodes (Policy II). Further flexibility is introduced by enabling capacity sharing among facilities with prearranged links (Policy III). Unlike these policies, which optimize capacities as proxies for production decisions prior to demand realization, Policies IV and V defer production decisions until demand is realized. The robustness and resilience of these policies are evaluated under varying levels of demand uncertainty and risks of supply disruptions. To provide actionable insights, we develop robust two-stage optimization frameworks for the proposed policies and design efficient methods to address varying uncertainty budgets for both supply and demand risks. Our results reveal that under demand uncertainty alone: (i) Capacity-sharing links among facilities yield the highest cost savings across all uncertainty budgets due to the pooling effect and significantly reduce shortage probability (Policy III), and (ii) production postponement offers only marginal benefits, highlighting the greater importance of upstream capacity-sharing over postponing production, particularly for moderate uncertainty budgets. Under simultaneous demand and supply risks, we demonstrate that (iii) capacity-sharing retains its value, while flexibility from production postponement deteriorates performance, favoring partially flexible networks with only capacity-sharing links over fully flexible ones with both sources of flexibility. To contextualize these findings, we apply the most effective policies to a real-world case study, quantifying their impact and providing design recommendations. Finally, we extend our models to an event-wise ambiguity set and demonstrate that by (iv) leveraging the supermodular structure of the second-stage problems, one can solve instances up to eight times larger.

Amir Ardestani-Jaafari is an associate professor at Faculty of Management, University of British Columbia. He is also Principal’s research chair in data-driven operations management. His primary research interests lie at the interface of healthcare operations, supply chain management, and optimization under uncertainty. His research has been published in top-tier journals, including Operations Research, Management Science, Transportation Science, INFORMS Journal on Computing, and Production and Operations Management, among others.  Dr. Ardestani-Jaafari’s research is funded by Mitacs, NSERC, SSHRC and others