The demand and current characteristics of tourism within globalized scenarios have prompted the introduction of intelligent systems in the decision making process of tourism companies, above all systems oriented to the approach and conformation of packages offer to potential customers. Selecting suitable information technologies that meet complex requirements from customers and travel agencies has become a top priority. Hence the use of smart algorithms and even expert systems is necessary as the means of support to the generation of the abovementioned tourism products. This paper explores the state of the art based on the Kitchenham-Charters guidelines and the PICOC method on the use of artificial intelligence tools and expert systems that contribute to the preparation of specialized tourism products, being of particular interest those systems designed according to a set of rules. This paper aims at presenting the most outstanding surveys following the criteria of the systematic review.

In this paper, the problem of obtaining optimal routes on tridimensional environments is discussed. This scenario is called as Traveler Salesman Problem (TSP 3D-variation). As is widely known, TSP has NP-complexity so is necessary to apply techniques to solve it approximately (no exacts solutions available). The purpose of this research is to present a genetic algorithm to solve 3D-TSP variation. These kind of evolutionary algorithms are ideal for solving complex problems where necessary rearrangements and route optimization. In case of genetic algorithms, optimal solutions appear faster depending on the quality of initial population, so theory recommends using metaheuristics for generating this population. In this study, it has used a metaheuristic GRASP algorithm to generate the initial population and, over it, apply the genetic operators proposed for optimizing individuals obtained. The results have optimal routes of movement and displacement and are directly applicable in the storage industry.