## 1 Introduction  
In recent times, shared mobility has spread and gained a lot of attention all around the world. Following the definition of a major organism like the United States Department for Transportation, it can be defined as a form of mobility that allows people to move through the shared use of vehicles (e.g., cars and bikes) on an as-needed basis and over short time periods [1], implementing the paradigm of Mobility-as-a-Service (MaaS) and pursuing a more sustainable way of moving in cities [2, 3]. A major advantage of shared mobility is that it contributes to reducing the spread of car ownership, in turn contributing to decreasing traffic congestion, air pollution and CO\({}_{2}\) emissions, and thus benefitting all the urban community and not just individuals [4, 5, 6]. During the last decade, besides more traditional sharing services like car-sharing and bike-sharing, which, as the name clearly suggests, put cars and bicycles at the disposal of users, electric scooters (e-scooters) sharing services have experienced huge diffusion and development and have been launched in many cities throughout the globe. The spread of e-scooters, both in sharing and privately owned, has been favored by the fact that these vehicles are cheap to buy and maintain, easy to ride and park, and are very agile in city traffic due to their tiny size. Such features have been so appreciated that some recent studies have pointed out how e-scooter riding has substantially replaced biking and walking and reduced the use of cars in some cities [7, 8, 9]. For an exhaustive introduction to e-scooter mobility, we refer the reader to [10, 11]. It has soon become apparent that the straightforwardness of riding and parking of e-scooters is a major advantage but also constitutes a major source of risks and issues: ridershave shown a tendency to hazardous driving behaviours and wild parking habits, which have led to an increase in accidents and decrease in urban decorum, as highlighted in many recent studies (e.g., [12; 13; 14]). As a consequence, municipalities have taken countermeasures, inflicting fines on e-scooter sharing companies and even banning them, e.g., in the French capital city of Paris [15; 16]. For tackling such problems, a number of strategies have been proposed, ranging from more effectively controlling the flows of e-scooters through optimized geofencing (e.g., [17]), to better designing e-scooter regulations (e.g., [18; 19]), to deploying staff for specifically correcting the wild parking of e-scooters and pursuing urban decorum (so-called \"beautificators\", see [20]), even with the support of drones [21]. Another major strategy, on which we focus in this work, is represented by acting on parking availabilities and designing parking lots that are specifically reserved for e-scooters. This topic is not new in the literature, but we note that it has received limited attention for the e-scooter case. In [22], the Authors propose a fuzzy-based multi-criteria decision framework for the identification of e-scooter parking locations in urban areas, taking into account twelve criteria and evaluating three possible alternatives (i.e., free-floating operations, docking-station-based operations and hybrid operations, including geo-fences hubs located in catchment regions of public transportation). In [23], a data-driven approach is instead proposed, aimed at establishing the position of a given number of e-scooter parking facilities through a clustering algorithm that attempts to maximize a measure of trip capture. Also, ref. [24] proposes a data-driven approach for the identification of city areas characterized by high e-scooter parking demand and road segments with high e-scooter traffic, on which interventions by local authorities should be focused, improving infrastructures and concentrating the enforcement of road rules. However, we note that there is still a gap to fill concerning the adoption of an Integer Programming-based approach for optimal e-scooter parking location and design.