The freight distribution in urban areas is crucial for connecting the producers with the end consumers. Therefore, the e-market and energy use relationship is crucial for developing sustainable urban logistic plans. However, some aspects, such as urban sprawl across the territory or customer habits, can make the process inefficient. Additionally, the growth of e-commerce (about 20% in the last ten years, [1]) has contributed to increasing freight distribution over short distances, thus requiring approaches and measures able to minimize the impacts. This topic has been widely studied by numerous researchers who have highlighted the benefits and the critical issues connected to it ^[2][3][4][5][2,3,4,5], pointing out the appropriate measures to take [6] and the assessment procedures [7]. The measures range from governance (e.g., [8]) to infrastructural elements (e.g., [9]) to equipment/new technologies (e.g., ^[2][10][2,10]). As an example, urban distribution centers (UDCs) are an element that could contribute to the reduction of impacts [11], especially if combined with low/zero-emissions freight vehicles ^[12][13][14][12,13,14]. This may contribute to the reduction of traffic congestion (since, in general, the vehicles moving from UDCs are fewer and smaller [15]), positively impacting the environment and urban life. Still, the general pattern of home deliveries (i.e., the freight vehicles that move into the city to serve the final clients, [16]) remains unchanged. A further level of the problem is the need to deliver to geographically particular places (for example, places that are difficult to reach with a truck and/or with a low population). To overcome this, pick-up points (e.g., local shops where clients can pick up their parcels), drone services and parcel lockers (clients pick up the parcels themselves) can be valuable alternatives. Thus, quantitative study on the potential interest of consumers in applying automatic delivery strategies is necessary to capture consumers’ future intentions, not underrating desire and expectation and possible consequences connected with choices [17].

Commercial establishments (newsagents, tobacco shops, and bookshops) can be used as hub points, thus representing delivery–collection facilities. This solution overcomes shortfalls connected with missed deliveries, providing a flexible collection method that allows the customers to have the package delivered to a location of their choice (e.g., near home or near work). A possible disadvantage (but it does not necessarily happen) is the queue that the customer can find in the commercial establishment (which must carry out its primary activity in addition to acting as a pick-up point).

The use of drones to provide delivery services in city centers and in particular geographical areas (or low-populated zones) is a measure considered in various papers (e.g., ^[18][19][20][18,19,20]), which note that the integration of drones into logistic plans is, up until today, limited by technological limitations and financial costs.

Parcel lockers are a reliable alternative to reduce the problems of home delivery services, being an effective last-mile distribution strategy (mainly if located in attended areas). The goal of using a parcel locker [21] is to reduce the distance travelled by freight vehicles and provide consumers with a flexible time window to collect their purchases (the locker is equipped with an electronic lock whose variable code is known by the customer). However, this approach could limit the size and the type of freight to be delivered and require some initial investments. In the literature [22], a classification of the use of parcel lockers is provided by considering the owner of the locker (e.g., carriers, companies, public providers), and also by considering the potential demand (customers) of those who could use the locker ^[17][23][17,23]. Hence, locker size and location issues must be solved ^[24][25][24,25].

2. Freight Distribution in Small Islands

The literature on urban freight distribution ranges from simulation models ^{[26][27][28][29][30]}[26,27,28,29,30] to optimization models ^[31][32][33][31,32,33], taking into account environmental and technological aspects ^[34][35][36][34,35,36] and distribution strategies ^{[37][38][39][40]}[37,38,39,40]. Since the use of parcel lockers has emerged as one of the possible distribution strategies, the number, size and location of parcel lockers require attention [24]. As the first to study this, Yuen et al. [41] examined the determinants that influence customers to accept parcel lockers: it emerged that customers consider elements such as service reliability, time flexibility and privacy; furthermore, they are willing to use the lockers if the extra charges that result are low. Such costs include the collection of information on using parcel lockers, the study of the locker interface and the cost of traveling to the locker location. Mitrea et al. [42] estimated customers’ attitudes toward lockers, highlighting that the most relevant factors are related to the environmental sustainability of the lockers and the customers’ willingness to carry the parcels themselves. Tang et al. [43] pointed out how both location and reliability of services, as well as flexibility and low fares, positively influence customers’ intentions to use automatic delivery solutions. Similarly, from Lai et al. [44], results show that time savings, security and reliability are significant predictors in analyzing the customer’s perception of the parcel locker. An et al. [45] highlighted that a positive attitude towards new technology and innovation and the ease of use are determinant factors that motivate people to approach services offered by e-procurement. Most of the interest in this topic concerns the locker position, the optimization of the courier trips to the locker location, and the integration with the traditional last-mile distribution, considering both mobile (autonomous or not) and fixed parcel lockers. Therefore, not intending to be fully exhaustive, rwesearchers consider some works dealing with parcel lockers in last-mile distribution below. Mobile parcel lockers (autonomous or operated by a human driver) represent a novelty for which there are prototype vehicles or patents, [46]. Li et al. [47] implemented a two-echelon vehicle routing problem to optimize both the travel of mobile lockers and the courier travels to restock them. Schwerdfeger and Boysen [46] proposed a dynamic location problem to allocate mobile parcel lockers to minimize the number of lockers. Wang et al. [48] proposed using mobile parcel lockers to meet demand, thus minimizing the costs, and optimizing the locker locations and the routes followed by the lockers. Then, they solved the same problem similarly under stochastic demand [49]. Considering the parcel locker in a fixed position, Lachapelle et al. [50] proposed a method based on logistic regression to locate the parcel lockers in the study area by considering accessibility, population density and customer characteristics. Che et al. [51] proposed a multi-objective model (maximize the covered demand, minimize the overlap between catchment areas of different parcel lockers, maximize the load of each locker) to optimize the location of parcel lockers, implementing a heuristic procedure to solve the problem. Similarly, Lin et al. [52] formulated a parcel locker location problem from a logistics company’s perspective to maximize profit. The approach used a discrete choice model to predict the probability of using the parcel locker. Peppel and Spinler [53] developed a model to design the location of fixed parcel lockers by minimizing emissions and costs during delivery operations. Prandtstetter et al. [54] investigated the benefits of parcel lockers, considering in the model the travels of clients from home to the locker and the variation in pollutant emissions. Furthermore, Bonomi et al. [55] proposed a location problem to minimize the environmental impacts. In this approach, a vehicle can deliver either to the client’s home or to the parcel locker station. Enthoven et al. [56] proposed a two-echelon vehicle routing problem to minimize costs by optimizing locker location and routes to serve customers; in particular, in the first level, a truck travels from a depot to a locker location or to a satellite location: in the first case the customers themselves collect the order, in the latter the delivery is assigned to a light freight vehicle. Luo et al. [57] formulated a model to optimize the position of the locker stations and the number of lockers (minimizing the cost and maximizing customer accessibility). Orestein et al. [58] formulated a parcel delivery problem to optimize the number of vehicles and assign them the best route. Pan et al. [59] presented a model to design a delivery network with parcel lockers when more than one depot is available. Gadheri et al. [60] crossed parcel locker services with crowd-shipping: specifically, multiple shippers use lockers as exchange points. The proposed heuristic procedure allowed the assignment of the delivery tasks to different stakeholders. Fessler et al. [61] presented some results of a test designed for placing parcel lockers in public transport stations; the experiment involved the passengers in the parcel delivery from one locker to another. Zhang et al. [62] proposed an approach wherein the parcel lockers are still an exchange point, and crowd shippers use public transport to deliver the parcel. dos Santos et al. [63] solved the same problem by considering a two-echelon approach wherein a crowd-shipper could support the courier. In this case, the parcels could also be transported there on off-peak hours, avoiding traffic congestion and reducing the impacts.