[eng] This article studies how to simulate the tourist flow in a city
from the point of view of the task allocation problem. We assume that all tourists start their tour from a cruise ship docked
in the Port of Palma. Then, each tourist decides which places
to visit and the order to do it. The tourist making decision
process to choose the path depends on a combination of two
stimulus, the utility to visit each place and the distance to go
there. The utility is a subjective perception modeled by the
stars and reviews downloaded from Google “Things to do” in
Palma and the distance is computed by the Vincentry distance.
Using several aggregation functions we merge this information to define the tourist response and, thus, to model how the
tourist feels stimulated to visit the different places of the city
over the time. The tourist decision making process is modeled by two different decision making methods, one of them
solves a subjective optimization problem and the other one is
based on sampling of possibility distributions. In order to generate the simulations, all the aforementioned ingredients are
put in common by means of multiple one-period possibilistic Markov chains which provide the route chosen by tourists
over time. The simulations and the model are implemented in
Python.