Towards better modeling of supermarkets

In this paper we introduce the supermarket setout problem which aims at finding an appropriate setout of products (i.e. which product should be placed on which position at which shelf) w.r.t. the expected profit, access time (time required to find a product), etc. Finding a good setout of products is important both in the customer-area and in the background store of supermarkets. The admissible setouts are constrained by laws (like food is not allowed to be placed next to chemicals) and "setout traditions" (even if two products are allowed to be placed next to each other by law, it may look "strange"). After taking all such requirements into account, there are usually still a great number of possible setouts that are generally greatly different from various aspects, like expected profit, customer satisfaction, or the time required to find a product. Therefore, finding an appropriate (closely optimal) solution is a crucial issue. Complex business problems, like the above supermarket setout problem, are often solved by means of artificial intelligence techniques that exploit results of advanced statistical analysis or data mining. In this paper, we develop a new algorithm for the supermarket setout problem. This is based on a combination of constraint satisfaction algorithms and frequent itemset mining (also known as market basket analysis).