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A. Mereuta
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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. ...
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. ...
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.
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.
Learning curves in machine learning are graphical representations that depict the relationship between a model's performance and the amount of training data it has been exposed to. They play a fundamental role in obtaining the knowledge and skills across a range of domains. Although there are already quite some researches studying machine learning curves, explaining the importance and practical application of learning curves, we still know very little about the factors that influence the parameters of the learning curve. The aim of this research is to give a better understanding of different factors affecting the parameters of the learning curve. Specifically, we are interested in how the dimensionality of a dataset can influence the parameters of the learning curve. Since learning curves are useful and have several applications, such as estimation of the time required to complete production runs, we would like to know if the dimensionality has any effect on the shapes of learning curves. To conduct the research I applied principal component analysis (PCA) three times with different amount of information preserved to reduce number of dimensions on several datasets and analysed the changes in the parameters of the obtained learning curves. The research showed that potentially there might be some relation between dimensionality and shape of the curve, but only in cases of specific machine learning model. The amount of experiments conducted is not sufficient to make solid conclusions and it is advised to continue with proposed experimental setup, but train machine learning models on increased number of datasets.
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Learning curves in machine learning are graphical representations that depict the relationship between a model's performance and the amount of training data it has been exposed to. They play a fundamental role in obtaining the knowledge and skills across a range of domains. Although there are already quite some researches studying machine learning curves, explaining the importance and practical application of learning curves, we still know very little about the factors that influence the parameters of the learning curve. The aim of this research is to give a better understanding of different factors affecting the parameters of the learning curve. Specifically, we are interested in how the dimensionality of a dataset can influence the parameters of the learning curve. Since learning curves are useful and have several applications, such as estimation of the time required to complete production runs, we would like to know if the dimensionality has any effect on the shapes of learning curves. To conduct the research I applied principal component analysis (PCA) three times with different amount of information preserved to reduce number of dimensions on several datasets and analysed the changes in the parameters of the obtained learning curves. The research showed that potentially there might be some relation between dimensionality and shape of the curve, but only in cases of specific machine learning model. The amount of experiments conducted is not sufficient to make solid conclusions and it is advised to continue with proposed experimental setup, but train machine learning models on increased number of datasets.