This paper proposes a new method to provide personalized tour recommendation for museum visits. It combines an optimization of preference criteria of visitors with an automatic extraction of artwork importance from museum information based on Natural Language Processing using textual energy. This project includes researchers from computer and social sciences. Some results are obtained with numerical experiments. They show that our model clearly improves the satisfaction of the visitor who follows the proposed tour. This work foreshadows some interesting outcomes and applications about on-demand personalized visit of museums in a very near future.
A first model developed in 2010 @cite_10 proposes to formulate the visitor routing problem as an extension of the open shop scheduling problem (in which each visitor group is a job and each interesting room is a machine). Each visitor group has to pass through all rooms but it is impossible for two groups of visitors to be simultaneously in the same room. This restriction can lead to non optimal or infeasible solutions if there are more visitor groups than rooms in the museum (which is the case if we consider each single visitor as a group).
@cite_10
2040552073
In the museum visitor routing problem (MVRP), each visitor group has some exhibit rooms of interest. The visiting route of a certain visitor group requires going through all the exhibit rooms that the group wants to visit. Routes need to be scheduled based on certain criteria to avoid congestion and or prolonged touring time. In this study, the MVRP is formulated as a mixed integer program which is an extension of the open shop scheduling (OSS) problem in which visitor groups and exhibit rooms are treated as jobs and machines, respectively. The time each visitor group spends in an exhibit room is analogous to the processing time required for each job on a particular machine. The travel time required from one exhibit room to another is modeled as the sequence-dependent setup time on a machine, which is not considered in the OSS problem. Due to the intrinsic complexity of the MVRP, a simulated annealing (SA) approach is proposed to solve the problem. Computational results indicate that the proposed SA approach is capable of obtaining high quality MVRP solutions within a reasonable amount of computational time and enables the approach to be used in practice.