Food waste is a major societal, environmental, and financial problem. One of the main actors are grocery stores. Policies for reducing food waste in those are complex due to a large number of uncertain heterogeneous factors like non-fully predictable demand.
Directly comparing food waste reduction policies through field experimentation is contrary to the very target of food waste reduction. This is why we propose RetaiL, a new simulation framework, to optimise grocery store restocking for waste reduction. RetaiL offers its users the possibility to create synthetic product data, based on real data from a European retailer. It then matches simulated customer demand to a restocking policy for those items, and evaluates a utility function based on generated waste, item availability to customers and sales. This allows RetaiL to function as a new Reinforcement Learning Task, where the agent has to act on restocking level given the state of the store, and receives this utility function as a reward.
In this demo, we let you open your own grocery store and manage its orders to the warehouse. Can you help in the fight against food waste?
Sami Jullien (University of Amsterdam)
Sebastian Schelter (University of Amsterdam)
Maarten de Rijke (University of Amsterdam)
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