Sequential task assignment is a crucial process in many contexts, where resource allocation over time is a key step to consider and often involves groups of people with diverse objectives, preferences, and constraints. Fairness in these scenarios is paramount, as it implies efficiency and satisfaction while also impacting performance. Although the definition of fairness depends on the context and domain, it generally ensures an equal distribution of tasks among participants, subject to certain constraints and guidelines. Moreover, it mitigates biases and disparities, promoting inclusivity and diversity within teams. In this paper, we highlight the different aspects of fairness in sequential task assignments and emphasize that the perspectives of various stakeholders must be considered. As motivating examples, we concentrate on two scenarios: (a) the timetable creation problem in the university domain, showing that the notion of fairness must be considered from both the students’ and professors’ points of view, and (b) the tourism traveling planning, where the perspectives of tour guides and tourists are taken into account during a planning process. We propose a generic formalization of the problem that an optimization algorithm can easily manage. The aim is to find and compare the fairness of different stakeholders and evaluate whether a fair solution for one of them can be fair for another with different constraints and preferences. We introduce the notion of local and global fairness to highlight that an optimal solution for one stakeholder does not necessarily mean it is optimal also for others, and some compromises need to be identified. Finally, we explore how global fairness can be achieved by integrating multiple solutions, each aligned with a local fairness perspective.

Understanding tourist behavior patterns is crucial for developing effective recommendation and decision support systems. The behaviors are often captured through the trajectories followed by tourists during their journeys or the sequences of visited Points of Interest (PoIs). Identifying common patterns and tracking their evolution over time can enhance the ability to understand, predict, and influence tourist choices, ultimately supporting goals like promoting specific destinations and fostering sustainable visitation patterns. Clustering algorithms like k-Means are commonly used to extract frequent patterns, requiring a tailored distance metric suited to the task. Since tourist trajectories combine spatial, temporal, and semantic features, defining a distance function that accurately captures these multifaceted aspects is essential. This paper examines various methods for encoding trajectory data and explores their effects on the clustering process. Finally, we compare and validate their suitability by using a real-world dataset of visits performed by tourists in Verona (Italy) from 2014 to 2022.