Grocery delivery company Picnic has identified affordable meal planning, especially in the context of recipe-based shopping, as an ongoing challenge faced by its customers. While recipes enhance customer experience and operational efficiency, Picnic currently lacks an algorithmic system to recommend recipes in a cost-effective way. This research addresses this gap by proposing a scalable optimization approach that minimizes the total cost of grocery products across a set of selected and recommended recipes.

To address this challenge, the paper formulates the problem as a Mixed Integer Linear Program (MILP), chosen for its ability to guarantee a globally optimal solution. The MILP model selects a combination of recipes and corresponding products that promote ingredient reuse and bulk purchasing to minimize overall cost. To complement this, the study implements two meta-heuristic models: a standalone Genetic Algorithm (GA) and a hybrid GA+MILP model, where GA selects recipes and MILP optimally assigns products. The optimization process also incorporates real-world constraints such as minimizing food waste and ensuring cuisine consistency.

Experimental results show that the MILP formulation consistently achieves substantial cost savings compared to a naive baseline, which selects the same set of recipes but does not optimize product choices across them. Gurobi outperforms CPLEX in solve time while maintaining identical solution quality. The standalone GA yields near-optimal solutions in seconds, while the hybrid GA+MILP model improves accuracy further, albeit with increased computational cost. When constraints such as waste reduction are added, the system remains effective, with one multi-objective variant reducing waste at minimal cost increase.

The findings confirm that cost-aware recipe recommendation is feasible, efficient, and adaptable. The proposed system offers a foundation for future extensions toward sustainability, personalization, and real-world deployment at Picnic.