As a part of the global plan to achieve environmental sustainability, eco-driving defines a set of driving behaviors that benefit both the drivers, by reducing fuel or energy consumption, and the environment, by mitigating emissions. This paper refers to urban eco-driving concepts for electric vehicles with regenerative braking, aiming to mitigate energy consumption on trajectories from their current state to a fixed final state. Such trajectories are determined as solutions of an optimal control problem having the acceleration of the electric vehicle as control input, and the position and speed as state variables. A power-based energy model is used to calculate the energy consumption and the energy regeneration while braking, resulting in a non-smooth model with some discontinuities. In order to solve the problem, different approaches are considered, based on different approximations of the power-based model. Such approaches are then extended to address the stop point problem, considering a traffic signal placed on the way of the vehicle. A comparative analysis is reported and discussed in the paper for different initial state and signal timing scenarios. In particular, it is shown that the analytical solutions obtained using a quadratic approximation of the energy model are very similar to the numerical solutions but they can be obtained with less computational effort, making them applicable in real-world applications.

In this paper we study the planning problem of loading trains in seaport container terminals, which consists in assigning containers to the wagon slots. The train load can be realized considering different objectives, in this work we focus on two very important objectives for terminal managers, that are the minimization of the number of reshuffles in the storage area and the minimization of the total distance covered by the handling equipment between the storage area and the wagons. To address this problem, some multi-objective methods are investigated in the paper, based on the$\varepsilon$-constraint approach and the lexicographic algorithm, allowing to find different sets of efficient solutions to be provided to train planners. The experimental tests, devised to compare the proposed multi-objective methods, show that the$\varepsilon$-constraint method integrated with a lexicographic approach is the most efficient choice since it allows to obtain a higher number of efficient solutions in shorter computational times. This method can be applied in decision support systems for train planners in order to allow them to decide the train load in an efficient way.Note to Practitioners—The automation and the process optimization in seaport container terminals is a relevant issue for terminal managers in order to face the strong competitiveness in the logistics and shipping sector and the new emerging problems due to the presence of mega vessels. Among the different operations to be optimally managed in a terminal, the loading of trains is becoming more and more significant, also due to the increase of freight traffic by rail, seen as a cheaper and more sustainable option compared with road transport. The optimal assignment of containers present in a storage area to the wagon slots is a crucial decision in order to reduce operational times and costs in a container terminal. In this paper, we address the train load planning problem considering two important objectives to be minimized, i.e. the number of reshuffles and the distance covered by the handling equipment. The multi-objective methods proposed in the paper are devised to find different efficient solutions in short computational times. The proposed algorithms are intended to be implemented within a decision support tool in which the solutions found are shown to the train planner who chooses the preferred option depending on personal evaluations and on the basis of information coming in real time.

The efficiency of freeway systems is often hindered by recurrent and non-recurrent congestion. The occurrence of these events is due not only to the high number of vehicles trying to use a shared infrastructure, but also by phenomena that temporarily deteriorate the capacity of the infrastructure itself. Among them, road accidents are considered as one of the primary causes of non-recurrent congestion. At the same time, several studies also identify traffic breakdowns as events leading to vehicle crashes. A specific goal of this research is to develop a global safety index that quantifies the expected number of crashes as a function of the current traffic state in the freeway system. Moreover, on the basis of this new index and the performance indicator generally adopted to evaluate the traffic delay, a coordinated ramp metering scheme is proposed jointly considering the reduction of travel times for the drivers and the improvement of safety in the freeway system. The control strategy is sought by defining a nonlinear optimal control problem with constrained control variables, solved by applying a specific gradient-based algorithm. The simulation analysis investigates the multi-objective nature of the problem assessing whether and to what extent the two components of the cost criterion are conflicting objectives.

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.

This paper deals with a distribution system with one origin and one destination in which the shipments are realized with a limited fleet of capacitated vehicles. An optimization problem is defined to find the optimal shipment policies in order to satisfy the external demand, by minimizing holding costs of the customer and transportation costs. Note to Practitioners - A distribution system with one origin and one destination characterized by a Vendor-Managed Inventory policy is considered. In such a system the supplier plans the deliveries to the customer in order to minimize the customer's inventory and the transportation costs. Besides the definition of some solution methods based on mathematical programming techniques, an algorithmic approach for solving the considered optimization problem is also derived in this paper. Even though this approach is suboptimal, it provides very effective solutions and its major strength stands in the possibility of easily implementing it without the use of mathematical programming solvers.

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.

Objective of this paper is to define an innovative control scheme to reduce congestion in freeway traffic systems via ramp metering. Such a control scheme is event-based and is composed of two control levels. At the lower level, Model Predictive Control is applied: the cell transmission model, reformulated in the so-called mixed-logical dynamical form, is adopted as internal model for the prediction and the quadratic deviations of the system variables from specific set-points are used as objective function in the finite horizon optimal control problem. The higher level of the control scheme consists of a discrete-event supervisor capable of distinguishing between regular and specific traffic conditions, adjusting the control law accordingly. More precisely, on the occurrence of classified events, the discrete-event supervisor determines the most suitable control action by making the controller switch among the elements of a set of model predictive controllers based on different internal models and cost functions.

A specific class of production systems is considered in this paper with the aim of minimizing inventory and production costs, over a finite horizon. In such a class of systems, all processes (arrival and departure of parts, production, demand to be satisfied) are modelled as piece-wise constant functions that change their values at asynchronous time instants. The decision variables are the constant rate at which raw parts enter the system and the service rate at the various time instants. In the paper, both the case of completely known demand and that of incomplete demand is taken into account. In both cases, a solution algorithm is provided in order to determine the optimal (or sub-optimal, in case of incomplete demand) solution of the optimization problem.

In this paper, supply chain nodes are considered, with the aim of optimizing the production of parts belonging to different classes (the case of two classes is here taken into account). The production node is modelled by a discrete-event model, being the system state, consisting of the continuous level of input and output inventories, affected by the asynchronous processes relevant to part arrivals and product departures. Moreover, also the resource capacity of the node is represented by a continuous variable (to be shared among the two classes). In the paper, two optimization problems are proposed: the former is only stated due to its intrinsic complexity, whereas the latter (a simplified version of the former) is stated and solved. In the latter problem, an optimization is performed each time an event occurs, in order to determine which the next event is, when the next event will occur, how many raw parts/products will arrive/depart, and which portions of production capacity are assigned to the two classes.