ISE Seminar Series - Seyed Mohammad Nourbakhsh

Multi-Year, Multi-Commodity Supply Chain Network Design

E-commerce retailers must decide, across a multi-year planning horizon, which fulfillment facilities should carry which commodities in order to meet delivery-time promises at minimum network cost. As demand grows and the commodity mix shifts over time, these decisions become increasingly complex, requiring simultaneous optimization of facility activation, inventory placement, and multi-commodity flow across a nationwide network subject to hard capacity, service-level, and time-in-transit constraints. The resulting optimization problem is large-scale, combinatorially intractable, and highly sensitive to the delivery promise window chosen by the retailer.

Dr. Seyed Mohammad Nourbakhsh is a Staff Research Scientist on the Applied AI team at Walmart Global Tech, where he leads data science and optimization initiatives for multi-billion-dollar omni-channel supply chain operations. He earned his Ph.D. in Transportation Systems and MS in Industrial Engineering from the University of Illinois at Urbana-Champaign in 2014. Over the past decade, Dr. Nourbakhsh has designed and deployed large-scale decision support, analytics, and optimization systems across major transportation and logistics sectors, including airline operations at Sabre, rail logistics at BNSF Railway, and retail supply chain network design at the world’s largest retailer.