Food package suggestion system based on multi-objective optimization: A case study on a real-world restaurant

Abstract Ordering dishes in a restaurant is a significant task, which determines not only the customers’ dining experience, but also the restaurant’s reputation. However, assisting customers in ordering a satisfying food package (FP), i.e., a combination of dishes, remains a challenge. First, local restaurants usually have very limited information about their customers, except the number of customers and their budget. Thus, suggesting FPs that satisfy their budget as well as surprise their palate is very difficult. Second, as a real-world function, FPs are required to be generated in real time while addressing several realistic issues such as dynamic dish inventories. In this study, we first extract knowledge from the history of orders of a restaurant, such as correlations among dishes, to formulate the FP suggestion as a multi-objective optimization problem. Thereafter, we propose a knowledge-based multi-objective evolutionary algorithm (k-MOEA) to tackle the problem and generate the suggested FPs. In addition, we develop an intelligent dish-ordering system (iOrdering), including several designed online and offline mechanisms to meet the real-time requirements of the FP suggestion services. Finally, the effectiveness of the k-MOEA is evaluated quantitatively by comparing it with three categories of baselines. Moreover, we have deployed the iOrdering system in a hot pot restaurant chain, and a real-world experiment demonstrates the advanced user experience of the devised system, including more than 77% acceptance rate of the suggested dishes and 66% saving of ordering time.