This paper focuses on two competing franchise chains and considers a situation in which each chain alternately determines location of stores they operate. In each period, the decision-making chain determines the location of new stores to be opened and the subset of existing stores to be closed in order to maximize profit. We assume two types of demand for services: point-based demand and flow-based demand. The point-based demand represents a customer that accesses a store directly, while the flow-based demand uses the service by stopping at a store along the preplanned travel path. It is assumed that a point-based customer chooses the closest open store, and a flow-based customer stops at each store along the travel path with equal probability. The objective function for the decision maker is defined as the profit obtained from covered customers for all stores, minus the cost of opening new stores and the cost of maintaining existing stores. We formulate the decision-maker's store location problem as an integer programming problem. Using this formulation, we investigate equilibrium locational patterns resulting from the competition of two franchise chains. Through numerical experiments using problem instances based on actual geographical and population data, we analyze (1) how stores in both chains are distributed in the final pattern, (2) how stores in both chains are opened/closed in each period, and (3) how basic input parameters of the model affect the final store distribution. The results showed that the chain that enters the market earlier than the other has a great advantage in the final profit.
We propose a method to analyze the large-scale ID-POS data of supermarkets collected transversely from various management agency. The method reveals store's sale types and customer's purchasing types, and clarifies a purchasing behavior and a tendency of its transitions in each store. At first, we pay attention to distributions of monthly amount of store's sale or customer's purchasing on the product categories, and extract store's sales types and customer's purchasing types, applying self-organizing map (SOM) to these distributions. At this time, we used Box-Cox transformation for an amount of money of a category, because these values are different for wrong number of digits. Then, we clarified the characteristic of the store group, using the composition ratio of the types of customer's purchasing, monthly transitional frequency between types, the number of the participation of the new customer, and so on. As a result, it became clear that a purchasing behavior and the tendency of transition between types of customer's purchasing, such as the rice, meat and fresh fish, were different between the store groups where varied in customer's royalty and age group of customers. It is thought that the proposed method is effective to discover a difference of the constituency of stores.