Applying AI to Improve Operations and Reduce Food Waste

Food waste and food affordability are two growing global challenges. Nearly one-third of all food produced worldwide is wasted, while many households struggle with rising food prices. PhD candidate Erfan Rafieikia at the Alberta School of Business, University of Alberta, is studying how data-driven decision-making can help address both issues at the same time.

Rafieikia’s research examines markdown platforms, digital marketplaces that sell near-expiration food products at discounted prices. These platforms help retailers reduce waste by selling products that might otherwise be discarded, while giving consumers access to more affordable food options.

A key focus of his work is understanding how delivery services could be integrated into these platforms to expand their reach. While markdown platforms can already reduce food waste, limited access to stores can prevent some consumers from benefiting from these discounts. By incorporating delivery options, the platforms could make affordable food accessible to a larger number of households while further reducing waste.

However, designing these systems presents a complex challenge. The platform typically sets delivery fees, while retailers determine the discount prices for their products. These decisions interact and influence how customers perceive the value of the offer and whether they choose to purchase the discounted food.

To study this interaction, Rafieikia models the system as a Stackelberg game, a leader–follower framework where the platform acts as the leader and retailers respond as followers. This structure is formulated as a bilevel optimization problem, allowing researchers to analyze how pricing decisions at one level affect outcomes at the other.

Using this approach, Rafieikia develops data-driven pricing models designed to balance platform sustainability, retailer profitability, and consumer accessibility. By improving how pricing and delivery strategies are coordinated, his research aims to create digital marketplaces that reduce food waste while making food more affordable for consumers.

Through this work, Rafieikia is demonstrating how advanced analytics and optimization techniques can support more sustainable and efficient food systems.

Authors: Erfan Rafieikia