New heuristic algorithms for discrete competitive location problems with binary and partially binary customer behavior

We consider discrete location problems for an entering firm which competes with other established firms in a market where customers are spatially separated. In these problems, a given number of facility locations must be selected among a finite set of potential locations. The formulation and resolution of this type of problem depend on customers' behavior. The attraction for a facility depends on its characteristics and the distance between the facility and the customer. In this paper we study the location problem for the so-called Binary and Partially Binary Rules, in which the full demand of a customer is served by the most attractive facility, or by all the competing firms but patronizing only one facility of each firm, the one with the maximum attraction in the firm. Two new heuristic algorithms based on ranking of potential locations are proposed to deal with this sort of location problems. The proposed algorithms are compared with a classical genetic algorithm for a set of real geographical coordinates and population data of municipalities in Spain.