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The COVID-19 pandemic has had strong economic repercussions on the industrial fabric of many countries around the world, with different degrees of impact depending on the level of severity with which the pandemic hit these countries. Italy has suffered a significant impact on its economy and export volumes have dropped dramatically for some product categories. The objective of this research is to understand how the pandemic has affected some selected logistics flows (i.e., fruit, automotive and machinery) transiting through the ports of Genoa and Savona, and to highlight the mitigation strategies that have been implemented during the pandemic period. The research was carried out analyzing customs and port data, as well as with the support of interviews and focus groups with experts of the Ligurian port sector. Finally, some policy indications are outlined with the aim of increasing the resilience of the supply chains analyzed in this research. These indications should help supply chains to be efficient and effective in forwarding flows of goods to/from ports even in the occurrence of unforeseen high-impact events.

The increasing use of e-commerce introduces major challenges for last-mile delivery, which is critical to ensure smooth functioning of supply chains. Besides the speed and cost of delivery, which are the most important factors for last-mile logistics, environmental and social sustainability factors are taking on an increasingly important role, especially in urban areas. In this perspective, last-mile delivery by drones could be a good choice both in terms of transport speed and sustainability. Being electrically powered, drones can reduce environmental impact; moreover, they enable the reduction of road congestion since they do not interfere with land infrastructures. The goal of this study is to analyse the viability of drones for last-mile delivery in the Italian city of Milan. A stated preference survey was carried out to assess end-user propensity towards drones. Using these results, a goods delivery service by drones was designed. Furthermore, a preliminary financial analysis was performed to evaluate the benefits for a company that would provide such a transport service. Findings show that last-mile delivery by drones can be successfully used to deliver small and light packages, reducing environmental and social impacts, and ensuring profits for the transport provider.

Making all types of public and private transport services available to users through a single and integrated digital platform is the new urban mobility paradigm called Mobility as a Service (MaaS). This scheme allows both a more sustainable urban transportation system and a more efficient transport service for users. For MaaS to be successful, its implementation should adapt to the specific features of the location covered by the service. The city maturity level has to be considered in terms of multimodality, infrastructures, regulations, user goals, and user willingness to use and pay for MaaS services. This paper discusses and analyses the results of a survey on MaaS conducted in three Italian metropolitan areas: Turin, Rome, and Genoa, which have very different and specific characteristics. The comparative analysis enables the definition of drivers, obstacles, and requisites for MaaS implementation to be successful. When it comes to cities with conservative mobility policies or cities that are new to MaaS experiences, resistance to adoption of the service might occur. The results of the research discussed in this paper can be a useful resource to enable decision makers to develop more effective and efficient transportation policies.

Efficient managing truck flows has become a challenge for container terminals that have to maintain a high productivity while guaranteeing high service levels to truckers. Moreover, the increasing quantity of cargo to be handled in short time windows introduces congestion and security issues. This paper deals with the definition and simulation of various algorithms implementing a truck management system, with the objective or reducing congestion outside and inside the terminal, so guaranteeing security standards. An extensive simulation campaign has been performed on real data sets provided by an important Italian container terminal, demonstrating the effectiveness of the proposed mathematical approach in reducing terminal congestion and security issues. The designed methodology is general enough to be effectively applied in other container terminals realities located worldwide.

People have always moved guided by the need to carry out various activities in different places, including that of meeting and communicating with other people. Over the past two decades, the concept of “communication” has significantly evolved given the introduction of multimedia digital technologies, which have enabled people to communicate without necessarily their physical presence. During the COVID-19 pandemic, “virtual mobility” (or “virtual communication”) played a crucial role in ensuring communications between people in different contexts of social life, growing in few months to previously unforeseeable levels and demonstrating that it can substitute physical movements in many occasions. A smart management of mobility that combines the ability to virtually communicate and the use of Intelligent Transportation Systems (ITS) to support physical mobility, can therefore strongly influence people's choice to move or not, and how. Starting from the analysis of physical mobility and virtual communications trends before and during the health emergency in Italy, this paper analyses the relationships between these two forms of communication, evaluating how virtual communication affected the different segments of Italian mobility during the pandemic and how it will affect the way people move in the post COVID-19 period. A SWOT analysis of virtual mobility is performed for each communication segment, with the aim of highlighting its pros and cons, but also future opportunities and possible threats. Some policy indications are also provided in relation to different mobility segments, governance levels (urban, regional and national) and congestion/pollution scenarios, highlighting how virtual mobility can help regulate physical movements, with the ultimate goal of pursuing a safe, sustainable, effective, efficient and connected mobility.

The evolving safety regulation is pushing seaports to comply with safety measures for workers performing heavy loads handling and repetitive movements. This paper proposes a risk-aware rostering approach in maritime container terminals, i.e., it addresses the rostering problem of minimizing and balancing workers’ risk in such terminals. To this end, a mixed integer mathematical programming model incorporating workforce risks is proposed, considering constraints such as the satisfaction of the workforce demand to perform the terminal operations, the worker-task compatibility and restrictions on the sequence of tasks assigned to the same worker. The model has been successfully applied to plan workforce over a six months horizon in a real container terminal located in Northern Italy, the Southern European Container Hub (SECH) in Genoa. As the workforce demand in SECH terminal is available at most two weeks in advance, a rolling horizon planning approach is devised. Experimental tests on real data provided by SECH terminal over a six months planning horizon highlight the effectiveness of the approach - the maximum monthly risk for workers is reduced by 33.9% compared to the current planning – and suitability to other container terminal contexts. Moreover, the model is applicable to a broad range of port situations, and robust enough to need little adaptation.

Promoting the adoption of plug-in hybrid electric (PHEVs) and battery electric vehicles (BEVs) is essential to meet local environmental requirements. A way to favour the use of these vehicles is to improve the ability to recharge vehicles at home or at the workplace, and to predict their residual energy state of charge throughout the day. This paper presents a novel user-friendly methodology to assess different recharging scenarios. The methodology was applied to a real case study on an international car manufacturer based in Italy. Several scenarios were addressed regarding the recharge at home of electrified vehicles, considering meter upgrades and different recharging speeds. The Vehicle-to-Grid (V2G) case was also investigated. The results show that PHEVs are the most flexible solution where domestic slow recharging is the only or preferred choice. BEVs become viable when at least a 4.5 kW supply contract is available together with a fast-recharging infrastructure with a suitable home grid, or where daytime parking lots with recharging facilities are available. The application of the proposed methodology to a real case study suggests that this approach can usefully help decision makers to identify the type of investments to be made and where they should be carried out.

The present paper faces the train load planning problem in container terminals. The problem consists of assigning containers to rail wagons while maximizing the total priority of the containers loaded and minimizing the number of rehandles executed in the terminal yard. Two different heuristic approaches, based on an innovative way to compute weight limitations and on two 0/1 integer programming models, are proposed and compared on the basis of specific key performance indicators. The heuristic approaches are compared using random generated instances based on real-world data. An extensive computational analysis has been performed.

This paper proposes an optimization approach for sizing port rail networks and planning railway shunting operations by adopting a discrete-time model of the overall system. First, a mixed-integer linear mathematical programming problem is defined in order to optimize shunting operations to be performed on the considered network by satisfying certain arrivals and departures of import and export flows. Moreover, the proposed procedure can be used to evaluate the capacity of a port rail network, in terms of maximum number of trains that can be managed over a certain time horizon, and to carry out what-if analyses aimed at testing different scenarios. The effectiveness of the proposed approach is shown by applying the optimization problem to a real case study referred to the port rail network of La Spezia Container Terminal located in Northern Italy. A computational analysis realized by varying the dimension and complexity of the problem instances is also reported in this paper to discuss the computational performance of the proposed model.

Nowadays the majority of goods passing through seaports are transported by road, resulting in a large number of empty movements and high total costs. This paper proposes an optimization model for the cooperative planning of multiple truck carrier operations in a seaport environment for maximizing the total profit derived from their cooperation. A compensation mechanism is introduced to motivate carriers to share their trips. Time windows, trip deadlines and fleet sizes are considered. The planning approach is evaluated using real data sets from the Italian port of Genoa. Numerous scenarios are tested and an extensive computational analysis is reported.

This paper presents an optimization approach for sizing the capacity of port rail networks, in terms of maximum number of trains that can be managed over a certain time horizon. The proposed optimization method is based on a discrete-time model of the overall system in order to represent the shunting operations in the port rail network. The resulting MILP optimization problem has been applied to a real case study referred to the port rail network of La Spezia Container Terminal, in Northern Italy. What-if analyses have been carried out to test the system potentiality by varying some parameters, i.e. the terminal equipment productivity, the number of locomotives and the time to perform some technical operations.

This paper relates with the assignment of trucks to time slots in container terminals equipped with Truck Appointment Systems. A two-phase approach is provided: first, export and import containers are matched in tuples with a clustering analysis to reduce the number of empty trips and, then, tuples are assigned to time slots to minimize trucks deviation from their preferred time slots and truck turnaround times. Real case instances related to Mexican and Italian container terminals are tested. Results show that our approach reduces empty-truck trips up to 33.79% and that it can be successfully applied to any container terminal.

Truck congestion is one of the most critical problems that affect many port realities around the world. To alleviate the truck congestion problem in container terminals, several approaches have been proposed and implemented. One of them refers to Truck Appointment Systems (TASs), which are technological platforms aiming to coordinate truck flows at ports, by supporting the scheduling of truck arrivals and truck operations. In this paper an integer programming model is formulated to schedule the operations of trucks that have to pick up or deliver containers in a container terminal operating with a TAS. The mathematical model seeks to assign appointments to the different time slots composing the gate opening time, with the goal of optimizing terminal resources, guaranteeing a predefined service level to haulers (i.e. maximum turnaround times in the container terminal), while minimizing congestion inside the terminal. The validity of the proposed optimization approach is highlighted by an experimental campaign based on real data sets provided by some Italian and Mexican container terminals. The obtained results highlight that the proposed model is efficient in reducing waiting times of trucks, improving both the operation efficiency and the accuracy of resource allocation in container terminals. Particularly, it can be observed that the terminal vehicle capacity and the truck arrival pattern are the most significant factors that affect the total truck delay.

Road transportation represents the most used transportation mode to cover short distances. However, structural lack of planning and optimization in road transportation creates negative effects both for companies and for the social community, such as environmental pollution, economic loss and road congestion. These effects are mainly due to the fact that a lack of planning can yield the necessity of a huge number of empty trips. Usually trucks that pick up or deliver a full container in a port must return back the empty container to the place where the trip started, so performing one leg of the total trip without payload. The aim of the present paper is to propose a mathematical approach for combining multiple trips in a port environment (specifically, import, export and inland trips) by considering the opportunity of carrying two 20ft containers simultaneously on the same truck and by using the same load unit if possible. In this way, in the same route, more than two nodes can be visited with the same vehicle thus significantly reducing the number of total empty movements. Time windows constraints related to companies and terminal opening hours as well as to ship departures are considered in the problem formulation. Moreover driving hours restrictions and trips deadlines are taken into account, together with goods compatibility for matching different trips. An experimental campaign based on real data is discussed in the paper.

In the present paper the issue of horizontal cooperation in a road transportation network is tackled. Different road trips belonging to a set of carriers that form a coalition are shared, with the final goal of maximizing the carriers' profit. The higher the number of carriers belonging to the coalition, the greater the possibility of obtaining profitable trip combinations; however, this makes also the cost sustained for managing the coalition increase. In this work, the proper size of the carrier coalition with the final goal of further improving the coalition profit is obtained by using an optimization based scheme. At first, a mathematical model is formulated with the goal of finding the best trip combinations to minimize the total costs for performing trips. Then, a second mathematical model is stated in order to assign both combined and single trips to carriers. This second optimization problem is solved several times for different number of carriers participating to the coalition in order to determine the best coalition size. An experimental campaign based on real data sets has been performed to validate the proposed approach. Various instances considering different number of trips and different values of the coalition management cost have been analyzed.

This paper regards the cooperation among truck carriers in logistic networks which include a port and some inland terminals. A set of carriers having different transportation demands is considered and the case in which the demand is shared among the different carriers is addressed. The problem of determining the best possible combinations of trips to satisfy the overall demand and their assignment to carriers is stated and optimally solved. The problem has the form of a nonlinear problem in which the objective is the maximization of the profits of the carriers involved in the collaboration, taking into account a set of operational constraints. Moreover, the problem concerns a multi-period planning in which trips can be performed early or late compared to their due dates and also compared to the day in which they are expected to be executed. Some experimental results are reported in the paper.

This paper proposes a heuristic approach for planning the activity of multiple carriers cooperating with the goal of eliminating empty truck trips while maximizing the cost saving resulting from their collaboration. The approach foresees three main phases: in the first step, the transportation demand is decomposed in two parts based on freight flows trade-off; in the second step, a linear optimization model, which takes into account compensation mechanisms among carriers, allows to combine two by two trips belonging to different carriers in order to decrease the number of empty movements. In this second phase, the importance of customers is explicitly taken into account by assigning to each trip a preference value. Finally, in the third step, a second optimization problem enables assigning, for each carrier, trucks to trips with the goal of minimizing their travel costs. The proposed heuristic approach has been tested on some instances and the results obtained are analyzed and discussed in the paper.

A container terminal is devoted to transfer containers from sea to land and vice versa in the most efficient way. The main objective of a container terminal is to optimize, in terms of times and costs, the activities related to container handling and forwarding. As regards the hinterland side, a crucial activity is represented by train loading/unloading, considering also the increasing importance assumed by rail transportation in allowing the move of large volumes of goods in/out of the logistic transportation nodes. This article pursues the modelling and comparison of different train loading policies by varying the storage area strategies in order to evaluate them and determine the most effective policy for ensuring quickness and efficiency in the terminal. With respect to the train loading policies, all the possible configurations are taken into account, starting from a pure sequential loading by the rail overhead travelling crane, which consequently implies some rehandling operations in the yard, up to the opposite situation in which non-sequential loading operations are allowed in order to avoid rehandling activities. These loading policies are analysed by varying the stacking strategies adopted in the yard where containers can be stored taking into consideration different factors (commercial priorities, weights, and so on). By using real data in relation to the physical characteristics of containers, trains and wagons, a proper experimental campaign has been performed in order to test the effectiveness of the different scenarios described above.

Horizontal cooperation among truck carriers is the framework considered in the present paper. More specifically, the goal of this paper is to properly distribute the profits related to a set of carriers collaborating together for merging multiple trips. First, a mathematical model is set to maximize the total profit of the coalition of carriers. The solution of the resulting optimization problem provides the best way of satisfying the overall demand by maximizing the overall profit. The way in which the overall profit is, then, allocated to the different carriers, thus determining carrier individual profits, is analyzed by using five game theory (GT) allocation methods. The main contribution of this work stands in the application of GT for the profit allocation; in the literature the majority of work deals with cost distribution. Moreover, the methods here proposed are practical and easy to be used by real trucking companies and have been implemented for real life cases. Results obtained in terms of individual and overall profits are reported and discussed in this paper.

For the undisputed degree of complexity that characterizes the port system, it certainly belongs to the category of systems of systems . In fact, the functioning of a port is made possible due to the interaction of a certain number of subsystems, which in turn depend on other subsystems. The goal of this paper is to model and simulate, by adopting the System Dynamics methodology, a subsystem of the port context: the rail port cycle. The model has been applied to some important container terminals located in Italy. “What-if” scenarios have been tested with the objective of underlining system bottlenecks.

This paper proposes a planning approach to optimize railway operations in seaport terminals by adopting a queue-based discrete-time model of the considered system. First, a mixed-integer linear mathematical programming problem is defined in order to optimize the timing of import trains and the use of the handling resources devoted to rail port operations. Second, in order to deal with unexpected situations or uncertainty in estimating some data necessary to the planning, an event-triggered receding-horizon planning approach is proposed, in which the finite horizon optimization problem is solved whenever a critical event happens or the real values of some problem data significantly differ from the predicted ones. Both these planning approaches are tested on data referred to a real terminal and deeply discussed in this paper.

The increasing importance of environmental and social issues in transportation imposes to focus more and more on strengthening and improving rail transportation. This is even truer for logistics nodes, such as seaports, which represent places where intermodality occurs. The present paper is devoted to model and plan the rail port cycle, at an aggregate level, with the goal of satisfying a given demand of trains outgoing from a generic container terminal. In order to do that, a discrete-time queue-based model resulting in a mixed-integer linear mathematical programming problem has been formulated; moreover, an event-triggered receding-horizon planning scheme has been defined in order to take into account possible disturbances affecting the system. The proposed approach has been applied to a real container terminal located in the Northern Italy coast; the results obtained show the effectiveness of the presented planning framework.

This paper proposes a heuristic decomposition approach for scheduling truck trips with the goal of satisfying the demand of a container single carrier while minimizing its costs. The approach foresees three phases: a pre-processing step, in which the demand is decomposed in two parts and a set of rules for combining trips are defined, and two optimization phases. The first mixed linear integer optimization model allows to combine trips two by two in order to decrease the number of empty or partially empty trips, while the second optimization problem enables assigning trucks to trips with the goal of minimizing travel costs. The efficiency of the proposed methodology is analyzed using a real simple case over a local network and the results are presented.

The main objective of this work is to model and optimize the rail cycle in seaport terminals. The model proposed in the paper represents the transfer of import containers from their storage in the yard until their exit from the terminal by train. At this purpose, the standing of containers and their movements inside the terminal are modelled by a set of queues, whose dynamic evolution is described by discrete-time equations, where the state variables represent the queue lengths and the control variables take into account the utilization of terminal resources (handling systems as well as tracks) and the timing of train movements from the terminal towards their destinations. On the basis of this model, an optimization problem is defined that consists in minimizing the transfer delays of containers in the terminal while satisfying specific real constraints. This model has been tested on real data regarding an Italian container terminal, as reported in the paper.

The particular complexity that characterizes a maritime port makes it belonging to the category of a system of systems. In fact, the functioning of a port is made possible thanks to the interaction of numerous subsystems, which in turn depend on other systems. The goal of the present paper is to provide a description of a port as a system of systems and to model and simulate the port rail process in some Italian container terminals, by using the software Powersim Studio, implementing the System Dynamics methodology. “What-If” scenarios have been tested with the objective of underlining the system bottlenecks and proposing improvement suggestions.

This work aims to analyze, with the System Dynamics simulation, the impacts of a new supermarket opening in terms of the traffic flows in the surroundings of a determined urban zone, in order to help decision makers to find the most suitable area and the proper number of car parks and counters to satisfy a certain customer level service, avoiding high waiting times and, consequently, customer losses. Among the different simulation methodologies, System Dynamics has been chosen because it allows to properly model systems that continuously change over time, that are affected by time delays and whose variables depend on their conditions at the previous time step. Traffic flow systems are included in this category of systems. Moreover, the software used for simulation, Power sim Studio™, allows the integration with GIS (Geographic Informative systems), which provides useful information on road mapping and users allocation.

This paper describes a simulation system according to improve steelmaking’s efficiency and to monitor its performances by anticipating the next period workload. Usually the production planning in those cases is made by the use of Gantt diagrams, based on operator’s work. This means that if an accident occurs, the operator himself has to change in few minutes the production plan with a lower performance than the original one. The first consideration is obviously that the operator’s experience itself it’s not sufficient to re‐plan a performing steelmaking chain. Hence the necessity of simulation as problem‐solving technique in this complex situation. A brief introduction on this paper is devoted to identify the common problems in most plants about production planning, and this is indeed needed to define the boundary conditions and the framework of the problem. Then, a description of steelmaking processes and the general features of critical aspects about steelmaking planning (Paragraph 2) is given in order to understand the bonds, features, criticalities to be analyzed and implemented in the simulation model. In paragraph 3 a detailed analysis of proposed methodology and system architecture is given in order to make the reader understand the complexity that the Authors had to face in modeling the system and the solutions they found with approximations, considerations, techniques and algorithms that were the most suitable to be used in this particular situation. A short description of the likely steelmaking plant modeled and Verification and Validation (V&V) results are carried in paragraph 4. It was in fact very important in such a complex system, to define the acceptability of results in terms of verification of the correctness, validation of the results, and accreditation to the users. This is a generally valid principle in simulation, but moreover in a complex system modeling such a steelmaking process, where an error can cost millions. At last, in paragraph 5, the Authors present the conclusions of this work, with a particular attention to the savings that the simulation can bring to this real system, explaining the results of the case study to which it was applied, and the possible future developments of this research are discussed. This model can anyway be applied to other realities than the one exemplified in this paper.This paper describes a simulation system according to improve steelmaking’s efficiency and to monitor its performances by anticipating the next period workload. Usually the production planning in those cases is made by the use of Gantt diagrams, based on operator’s work. This means that if an accident occurs, the operator himself has to change in few minutes the production plan with a lower performance than the original one. The first consideration is obviously that the operator’s experience itself it’s not sufficient to re‐plan a performing steelmaking chain. Hence the necessity of simulation as problem‐solving technique in this complex situation. A brief introduction on this paper is devoted to identify the common problems in most plants about production planning, and this is indeed needed to define the boundary conditions and the framework of the problem. Then, a description of steelmaking processes and the general features of critical aspects about steelmaking planning (Paragraph 2) is given in order ...

In this paper the problem of the strategic design of an automatic intermodal terminal is addressed. In particular, the considered terminal is supposed to be provided with an innovative transfer system (such as the Italian system Metrocargo) allowing to load/unload containers in a fast and horizontal way under the electric line. In this system, the main strategic decisions regard the number of shuttles, as well as the number of storage locations to be used in the terminal. At this purpose, an optimization problem is stated for determining the optimal resources in order to minimize the overall cost and taking into account some specific constraints. An algorithm is also defined for solving the proposed optimization problem.

If not properly managed neither planned on real needs, the maintenance of rolling stock may strongly affect rail operations in local public transport, risking to compromise the quality of service or generating an over sizing of the fleet. Therefore, an effective coordination is required between the Operation and Maintenance departments. Some flexibility in maintenance activities i.e., preventive and on condition maintenance policies- has already been applied for some years in the regional rail transport with successful results; however, it has not been introduced yet in rail public transport, where a corrective maintenance is generally adopted. In this work, the proper scheduling of more flexible maintenance activities in the rail public transport context is addressed through the use of discrete event simulation. Real data sets provided by the Italian GTT-Gruppo Torinese Trasporti company have been used to test the proposed approach and to carry out a multi-scenarios campaign, aiming at analyzing the effectiveness of the maintenance process when certain operating conditions change or unexpected events occur. Some improvement proposals have also been analyzed with the proposed simulation method.