Sport mega-events, such as the Soccer World Cup or Olympic Games, attract many visitors from all over the world. Most of these visitors are also interested in, besides attending the sports events, visiting the host nation and the neighboring countries. In this paper, we focus on the upcoming FIFA World Cup Qatar 2022. As per the schedule of the tournament, a national team can play 7 matches at most. Therefore, a supporter will have six short breaks (of three to five days) between consecutive matches in addition to two longer ones, immediately before and after the tournament, during which they can plan some touristic trips. We study the problem faced by a touristic trip provider who wants to offer a set of touristic packages, chosen among a very large set of options, devoted to World-Cup related tourists. The number of packages offered must be limited due to organizational reasons and the necessity to guarantee a high participation in each trip. In this study, a set of user profiles is considered. It represents different categories of tourists, characterized by different preferences and budgets. Each user is supposed to pick the packages that maximize their satisfaction, considering their budget and time restraints. The goal of the company is to choose the set of packages to be offered that would maximize the average users satisfaction. To address this NP-Hard combinatorial optimization problem we provide a mathematical formulation and a matheuristic, named Consensus-Based Kernel Search (CKS), wherein an alternative rule is used to create the initial Kernel and partition variables in buckets. Computational results evidence the excellent performance of CKS and prove that the newly introduced algorithm systematically outperforms the classical Kernel Search.
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