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Thesis Defence: Digital Marketplace Platform Inventory Management Under Disruption: a Robust Optimization/Regret Minimization Approach

December 1, 2023 at 9:00 am - 12:00 pm

Public Oral Defnce Amirhossein Salamirad

Amirhossein Salamirad, supervised by Dr. Javad Tavakoli, will defend their dissertation titled “Digital Marketplace Platform Inventory Management Under Disruption: a Robust Optimization/Regret Minimization Approach” in partial fulfillment of the requirements for the degree of Master of Science in Mathematics.

An abstract for Amirhossein Salamirad’s thesis is included below.

Defences are open to all members of the campus community as well as the general public. Please email javad.tavakoli@ubc.ca to receive the Zoom link for this defence.


Digital marketplace platforms are important because they provide low prices, easy accessibility, and global reach, which enable suppliers to create value and offer novel business models in e-commerce. Typically, large platforms engage in two types of contracts with their suppliers, wholesale and agency contracts, which define the terms of their business relationship and influence product distribution and pricing. In order to ensure a high level of service and maximize profitability and supply chain resilience, an optimal inventory management system is necessary. In this study, we present a robust optimization (RO) framework to determine the optimal inventory and ordering policy for each contract type based on the uncertain nature of demand and supply. Supposedly, RO is over-conservative. Hence, we propose an alternative framework, namely the maximum regret minimization (MRM), which is less conservative than the RO. The column-and-constraint generation method is used to test the proposed models under various scenarios with different demand correlation levels. As a method of solving these computationally intensive model, we propose a linear decision rule (LDR) for the wholesale-based MRM problem. Our LDR is shown to be a superior decision rule to existing ones through numerical results with which we are able to demonstrate encouraging managerial insights.


December 1, 2023
9:00 am - 12:00 pm

Additional Info

Registration/RSVP Required
Yes (see event description)
Event Type
Thesis Defence
Research and Innovation, Science, Technology and Engineering
Alumni, Community, Faculty, Staff, Families, Partners and Industry, Students, Postdoctoral Fellows and Research Associates