In the era of Industry 5.0, securing Industrial Control Systems (ICS) is increasingly vital, especially when relying on legacy communication protocols like Modbus TCP that may lack built-in protection mechanisms. This paper addresses the challenge of preserving the confidentiality of internal system states from potential cyber adversaries through a security-by-design framework. We propose a novel approach that leverages Discrete Event Systems (DES) theory to model communication flows and applies probabilistic opacity to quantify the risk of state disclosure. Central to our method is the concept of selective encryption: instead of encrypting all messages, we strategically encrypt only those events that could reveal sensitive information. This gives rise to a budget-constrained optimization problem, where the goal is to enforce opacity under resource limitations. To solve this efficiently, we develop a greedy algorithm that maximizes security by allocating encryption effort to the most critical events. The proposed method is validated using a representative example featuring two distinct query types, demonstrating its capability to limit information leakage while keeping low the computational overhead.

The demand for safe and ergonomic workplaces is rapidly growing in modern industrial scenarios, especially for companies that intensely rely on Human-Robot Collaboration (HRC). This work focuses on optimizing the trajectory of the end-effector of a cobot arm in a collaborative industrial environment, ensuring the maximization of the operator’s safety and ergonomics without sacrificing production efficiency requirements. Hence, a multi-objective optimization strategy for trajectory planning in a safe and ergonomic HRC is defined. This approach aims at finding the best trade-off between the total traversal time of the cobot’s end-effector trajectory and ergonomics for the human worker, while respecting in the kinematic constraint of the optimization problem the ISO safety requirements through the well-known Speed and Separation Monitoring (SSM) methodology. Guaranteeing an ergonomic HRC means reducing musculoskeletal disorders linked to risky and highly repetitive activities. The three main phases of the proposed technique are described as follows. First, a manikin designed using a dedicated software is employed to evaluate the Rapid Upper Limb Assessment (RULA) ergonomic index in the working area. Next, a second-order cone programming problem is defined to represent a time-optimal safety compliant trajectory planning problem. Finally, the trajectory that ensures the best compromise between these two opposing goals –minimizing the task’s traversal time and maintaining a high level of ergonomics for the human worker– is computed by defining and solving a multi-objective control problem. The method is tested on an experimental case study in reference to an assembly task and the obtained results are discussed, showing the effectiveness of the proposed approach.Note to Practitioners—Health and safety in workplaces are business imperatives, since they ensure not only a safe collaboration between industrial machinery and human operators, but also an increased productivity and flexibility of the entire industrial process. Hence, investing in health is a real driver for business growth. The key enabling technologies of Industry 4.0, such as collaborative robotics, exoskeletons, virtual and augmented reality, require standardization and indispensable technical safety requirements that cannot ignore physical, sensory, and psychological peculiarities of the human worker and aspects like usability and acceptability of these technologies in performing their activities. Against this ongoing industrial challenge, the aim of this paper is to provide researchers and practitioners with an innovative HRC trajectory planning methodology focused on enhancing production efficiency while respecting the SSM ISO safety requirement and guaranteeing the ergonomic optimal position of the operator during an assembly task. Therefore, the proposed methodology can be a convenient solution to be deployed in industrial companies, since it can support human operators by drastically reducing work-related musculoskeletal disorders and augmenting their performance in the working environment.

The design of the layout of Vertical Lift Module (VLM) warehouses is a non-trivial process that involves selecting dimensions, internal configuration, and allocation of each tray to avoid space loss while satisfying logistic constraints. Our contribution in this context is a two-phase matheuristics –an algorithm that combines exact mathematical methods and heuristics– to simplify the design of VLMs layout. The proposed matheuristics relies on three Mixed-Integer Linear Programming models, addressing the internal configuration of trays and the allocation of trays into columns based on industrial logistic constraints. This approach requires as input parameters the items features, predetermined tray types with different dimensions, matheuristic settings, and a priority rule for tray allocation. The algorithm outputs to the logistics operator types and quantities of trays needed, internal partitioning, item positions in each tray, and tray positions in each column. Extensive testing demonstrates the effectiveness of our approach under realistic scenarios. Additionally, we introduce a comprehensive set of priority rules for allocating trays into columns, providing a comparison to assist logistics operators in selecting the most suitable for specific scenarios. Note to Practitioners—VLMs are closed structures composed of columns that house a variable number of sliding trays and a lift-mounted module that handles the trays. Their operation revolves around the so-called “goods to the man” principle, where goods are automatically brought to the operator using a dedicated access bay. This design ensures that stored items are easily accessible to operators, reducing time for order picking and improving workplace safety and ergonomics. Designing a VLM for logistics companies is a complex and time-consuming task since it requires optimizing a target objective while satisfying a large set of constraints. The current manual approach lacks automated methods and relies on experienced operators and iterative improvement processes. Our two-phase matheuristic algorithm automates VLMs layout configuration, addressing tasks from item placement to tray allocation within columns. The algorithm is versatile, considering various practical and logistic constraints, making it applicable in warehouse design decision support systems or engineering software.

In distribution logistics, the planning of vehicles’ routes and vehicles’ loads are traditionally managed separately, despite these activities are correlated. This often leads to various re-designs to make the routes and load plans compatible and applicable in practice. Moreover, the planned routes, which are static by definition, cannot always cope with unexpected events. Traffic congestion, vehicle failures, adverse meteorological conditions, and further undesired events can make the planned routes inapplicable and requirevehicles’ re-routing. This results in lower service levels, undesired delays, and higher costs for logistics companies. With the aim of overcoming the above limitations, this work proposes a novel approach based on a matheuristic algorithm that jointly solves the problem ofdelivery planninganddynamic vehicle routingto automate the delivery process in a logistics 4.0 perspective. The presented algorithm includes two different phases: the static phase, which is executed offline and in advance with respect to the delivery day, and the dynamic phase, which is executed in real-time to cope with unexpected events during the delivery. For the first phase, a matheuristic approach is defined to efficiently solve the combined vehicle routing and loading problems. Differently, for the second phase, a genetic algorithm is proposed to re-route vehicles in real-time, considering both the redefinition in real-time of the nominal trip and/or of the sequence of the customers to be visited. The algorithm is tested both on a literature benchmark and on a real dataset provided by an Italian logistics company. The obtained results show that, on the one hand, the proposed algorithm can automatically provide feasible solutions that minimise travel costs, total travelled distance, and empty space on the vehicles; on the other hand, it can ensure in real-time effective re-routing solutions in case of unexpected events occurring during delivery.Note to Practitioners—This work is motivated by the need for facilitating the operations of planning and routing deliveries in the external logistics sector. We propose an algorithm that automatically generates feasible routing and loading plans for a set of Transport Units (TUs) (i.e., the static phase), and then updates in real-time the nominal route in case of unexpected events (i.e., the dynamic phase). More specifically, the first phase of the algorithm takes as input the set of different clients, the list of products packed into bins (i.e., standard packing units) to be delivered to each client, and the set of transport units available for the deliveries, and provides as output the number and type of TUs to be used, the composition of the bins in each transport unit, and the corresponding route, while optimising the space occupation in each TU and the travel costs. The second phase, instead, takes as input the nominal routes computed in the first phase and, in case of unexpected events (e.g., accidents, slowdowns, etc.) affecting one or more routes, it re-routes the involved trucks guaranteeing the maximum efficiency in regards to travel cost, travel time, and quality of service. The adoption of this algorithm by logistic companies supports the automation of the delivery process and drastically improves the efficiency of logistic operations, with particular regard to the number of used TUs, costs, safety of goods, and customers’ satisfaction.

This study introduces a novel control framework for human-drone interaction (HDI) in industrial warehouses, targeting pick-and-delivery operations. The goals are to enhance operator safety as well as well-being and, at the same time, to improve efficiency and reduce production costs. To these aims, the speed and separation monitoring (SSM) operation method is employed for the first time in HDI, drawing an analogy to the safety requirements outlined in collaborative robots’ ISO standards. The so-called protective separation distance is used to ensure the safety of operators engaged in collaborative tasks with drones. In addition, we employ the rapid upper limb assessment (RULA) method to evaluate the ergonomic posture of operators during interactions with drones. To validate the proposed approach in a realistic industrial setting, a quadrotor is deployed for pick-and-delivery tasks along a predefined trajectory from the picking bay to the palletizing area, where the interaction between the drone and a moving operator takes place. The drone navigates toward the interaction space while avoiding collisions with shelves and other drones in motion. The control strategy for the drone cruise navigation integrates simultaneously the time-variant artificial potential field (APF) technique for trajectory planning and the iterative linear quadratic regulator (LQR) controller for trajectory tracking. Differently, in the descent phase, the receding horizon LQR algorithm is employed to follow a trajectory planned in accordance with the SSM, which starts from the approach point at the border of the interaction space and ends in the volume with the operator’s minimum RULA. The presented control strategy facilitates drone management by adapting the drone’s position to changes in the operator’s position while satisfying HDI safety requirements. The results of the proposed HDI framework simulations for the case study demonstrate the effectiveness of the method in ensuring a safe and ergonomic HDI within industrial warehouses.

Cyber-Physical-Human Systems (CPHS) are complex systems centered on human beings. Currently, both industries and societies are even more dependent on CPHS, which have application in various fields and can have interactions of humans and CPS at various levels. In this context cyber-physical attacks become increasingly dangerous both for the physical plant and the human being. Thus, safety and security of CPHS must be simultaneously guaranteed, and the investigation of vulnerabilities of CPHS applications is of paramount importance to avoid any harm to human lives. In order to achieve this target, the current work presents a thorough analysis of the most recent literature in this field. The selected papers are classified by level of interaction between CPS and human (i.e., physiological, cognitive, and population-wide) and then by type of specific application, to investigate challenges, opportunities, and open questions. A detailed discussion concludes the paper reporting on the current advancements and promising future research paths.

Driven by the digital transformation required by Logistics 4.0, the use of automation in warehouses is constantly growing. In particular, robotic palletizers offer significant potential for optimizing warehouse operations, thanks to higher flexibility and throughput than traditional palletizing systems. Despite the availability of several solutions in the market, the optimal deployment of a robotic palletizer in warehouses is not straightforward: a design phase is needed to determine the most convenient configuration that ensures automatic palletizing is fully integrated into the warehouse processes. In this paper, we propose a simulation-based versatile tool for modeling and analysis purposes, aimed at supporting the design of a robotic palletizing cell in a bottom-up fashion. As a core methodology, we employ timed colored Petri nets, which allow - once the analysis on packing requirements and constraints is conducted - to rapidly model the system as a composition of basic subsystems, and implement alternative simulations to evaluate the corresponding performance and effectively benchmark the alternative configurations. The proposed approach is applied to a real case study, showing its effectiveness in identifying the solution that achieves a good compromise between the use of resources and the performance of warehouse operations.

Last-mile delivery is one of the most discussed problems of the last decade due to the growing importance of e-commerce and the development of Industry 4.0. In particular, this problem regards the delivery of parcels from the warehouse to the final customers. In order to bring efficiency and innovation, in this paper a hybrid delivery architecture is considered, which takes advantage of the combined use of a drone and a truck to perform a sequence of pick-ups and deliveries, and the problem of optimal control of the drones' missions is addressed. The reference scenario is the smart city where the drone of the hybrid delivery architecture is in charge of three different pick-up and delivery missions: truck to point (i.e., pick-up from the truck and delivery to the customer), point to point (i.e., delivery to a customer and pick-up from the subsequent customer), and point to truck (i.e., reentry from a customer to the truck). From the control point of view, the drone is optimally guided in all the operating modes, i.e., ascent and descent from/to truck mode, free flight mode with/without payload, and descent for pick-up/delivery mode, by a receding horizon linear quadratic regulator (LQR), which is able to manage the drone in the dynamic landing on a movable vehicle and to allow the changing in real time of the landing point on the truck. Simulation results of the truck-drone delivery architecture are presented and discussed in detail, proving the effectiveness of the proposed method.

This paper investigates a novel approach for properly modeling Hydraulic Servo Actuators (HSAs) based on ON-OFF switching valves. HSAs represent very high efficiency and small size-to-power ratio hydraulic actuators. Their functioning is guaranteed by their control system that ensures the desired flow-rate, and consequently, the proper pressure, to be provided to the actuator's chambers. Nevertheless, achieving a good model of such HSAs for control purposes is non-trivial, due to their hybrid nature inherited from the switching between the different operating modes produced by valves. To overcome this limit, we propose an average equivalent discrete-time model of the chambers' pressure dynamics related to a single control input for the digital valves. The proposed model takes inspiration from the analogy existing between hydraulic systems and power electronic converters, and guarantees the same performance as the traditional model, with the advantage of greatly simplifying the control of the servo actuator. Finally, the consistency of the proposed model with respect to its nonlinear hybrid version is proved via numerical examples.

This paper presents an application of human-drone interaction (HDI) for inventory management in a ware-house 4.0 that aims at improving the operators' safety and well-being together with increasing efficiency and reducing production costs. In our work, the speed and separation monitoring (SSM) methodology is applied for the first time to HDI, in analogy to the human-robot interaction (HRI) ISO safety requirements as well as the rapid upper limb assessment (RULA), for evaluating the operator's ergonomic posture during the interaction with the drone. With the aim of validating the proposed approach in a realistic scenario, a quadrotor is controlled to perform a pick and place task along a desired trajectory, from the picking bay to the palletizing area where the operator is located, avoiding collisions with the warehouse shelves by implementing the artificial potential field technique (APF) for planning and the linear quadratic regulator (LQR) and iterative LQR (iLQR) algorithms for tracking. The obtained results of the HDI architecture simulations are presented and discussed in detail proving the effectiveness of the proposed method for a safe and ergonomic HDI.

The increasing demand for freight transport requires logistic companies to improve their competitiveness by ensuring high service levels at limited costs. This paper investigates the problem of defining delivery plans with the aim to support logistic companies in reducing planning times and freight delivery costs. In delivery planning, given a set of delivery requests, both the routes and load configurations of Transport Units (TUs) are to be established. In the literature, this problem is defined as Three-dimensional Loading Capacitated Vehicle Routing Problem with Time Windows (3LCVRPTW). However, these problems are generally tackled separately and referred to as the vehicle routing problem and the container loading problem, respectively. Moreover, only a few contributions present solution approaches for real logistic systems, and these methods are mainly based on heuristics. In this work, we define a novel matheuristic algorithm for the integrated solution of the vehicle routing problem and container loading problem. The proposed method is suitable for real logistic applications and combines the advantages of exact solutions with the rapidity of heuristics. The approach aims at minimizing the total travel costs and the clients’ time windows violations in the routes’ definition, while optimizing the configuration of the cargo inside each TU. The developed matheuristic algorithm is tested both on a well-known literature benchmark and on a real dataset provided by the Italian company Elettric80. The obtained results show that the proposed method succeeds in determining in a short computational time both feasible routes and loading plans, minimizing the related costs while fulfilling logistics constraints.

This paper focuses on the control of microgrids where both gas and electricity are provided to the final customer, i.e., multi-carrier microgrids. Hence, these microgrids include thermal and electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power units. The parameters characterizing the multi-carrier microgrid are subject to several disturbances, such as fluctuations in the provision of renewable energy, variability in the electrical and thermal demand, and uncertainties in the electricity and gas pricing. With the aim of accounting for the data uncertainties in the microgrid, we propose a Robust Model Predictive Control (RMPC) approach whose goal is to minimize the total economical cost, while satisfying comfort and energy requests of the final users. In the related literature various RMPC approaches have been proposed, focusing either on electrical or on thermal microgrids. Only a few contributions have addressed the robust control of multi-carrier microgrids. Consequently, we propose an innovative RMPC algorithm that employs on an uncertainty set-based method and that can provide better performance compared with deterministic model predictive controllers applied to multi-carrier microgrids. With the aim of mitigating the conservativeness of the approach, we define suitable robustness factors and we investigate the effects of such factors on the robustness of the solution against variations of the uncertain parameters. We show the effectiveness of the proposed RMPC approach by applying it to a realistic residential multi-carrier microgrid and comparing the obtained results with the ones of a baseline robust method.Note to Practitioners—This work is motivated by the emerging need for effective energy management approaches in multi-carrier microgrids. The inherent difficulty of scheduling simultaneously the operations of various energy infrastructures (e.g., electricity, natural gas) is exacerbated by the inevitable presence of uncertainties that affect the inter-dependent dynamics of different energy resources and equipment. The proposed robust MPC-based control strategy allows the energy manager to effectively determine an optimal energy scheduling of multi-faceted system components, making a tradeoff between performance and protection against data uncertainty. The presented strategy is comprehensive and generic, as it can be applied to different microgrid frameworks integrating various types of system components and sources of uncertainty, while at the same time being implementable in any energy management system.

Railways are a well-recognized sustainable transportation mode that helps to satisfy the continuously growing mobility demand. However, the management of railway traffic in large-scale networks is a challenging task, especially when both a major disruption and various disturbances occur simultaneously. We propose an automatic rescheduling algorithm for real-time control of railway traffic that aims at minimizing the delays induced by the disruption and disturbances, as well as the resulting cancellations of train runs and turn-backs (or short-turns) and shuntings of trains in stations. The real-time control is based on the Model Predictive Control (MPC) scheme where the rescheduling problem is solved by mixed integer linear programming using macroscopic and mesoscopic models. The proposed resolution algorithm combines a distributed optimization method and bi-level heuristics to provide feasible control actions for the whole network in short computation time, without neglecting physical limitations nor operations at disrupted stations. A realistic simulation test is performed on the complete Dutch railway network. The results highlight the effectiveness of the method in properly minimizing the delays and rapidly providing feasible feedback control actions for the whole network.Note to Practitioners—This article aims at contributing to the enhancement of the core functionalities of Automatic Train Control (ATC) systems and, in particular, of the Automatic Train Supervision (ATS) module, which is included in ATC systems. In general, the ATS module allows to automate the train traffic supervision and consequently the rescheduling of the railway traffic in case of unexpected events. However, the implementation of an efficient rescheduling technique that automatically and rapidly provides the control actions necessary to restore the railway traffic operations to the nominal schedule is still an open issue. Most literature contributions fail in providing rescheduling methods that successfully determine high-quality solutions in less than one minute and include real-time information regarding the large-scale railway system state. This research proposes a semi-heuristic control algorithm based on MPC that, on the one hand, overcomes the limitations of manual rescheduling (i.e., suboptimal, stressful, and delayed decisions) and, on the other hand, offers the advantages of online and closed-loop control of railway traffic based on continuous monitoring of the traffic state to rapidly restore railway traffic operations to the nominal schedule. The semi-heuristic procedure permits to significantly reduce the computation time necessary to solve the rescheduling problem compared with an exact procedure; moreover, the use of a distributed optimization approach permits the application of the algorithm to large instances of the rescheduling problem, and the inclusion of both the traffic and rolling stock constraints related to the disrupted area. The method is tested on a realistic simulation environment, thus still requires further refinements for the integration into a real ATS system. Further developments will also consider the occurrence of various simultaneous disruptions in the network.

Deep drawing is a metalworking procedure aimed at getting a cold metal sheet plastically deformed in accordance with a pre-defined mould. Although this procedure is well-established in industry, it is still susceptible to several issues affecting the quality of the stamped metal products. In order to reduce defects of workpieces, process control approaches can be performed. Typically, process control employs simple proportional-integral-derivative (PID) regulators that steer the blank holder force (BHF) based on the error on the punch force. However, a single PID can only control single-input single-output systems and cannot handle constraints on the process variables. Differently from the state of the art, in this paper we propose a process control architecture based on Model Predictive Control (MPC), which considers a multi-variable system model. In particular, we represent the deep drawing process with a single-input multiple-output Hammerstein-Wiener model that relates the BHF with the draw-in of$n$different critical points around the die. This allows the avoidance of workpiece defects that are due to the abnormal sliding of the metal sheet during the forming phase. The effectiveness of the proposed process controller is shown on a real case study in a digital twin framework, where the performance achieved by the MPC-based system is analyzed in detail and compared against the results obtained through an ad-hoc defined multiple PID-based control architecture.Note to Practitioners—This work is motivated by the emerging need for the effective implementation of the zero-defect manufacturing paradigm in the Industry 4.0 framework. Especially in the deep drawing process, various quality issues in stamped parts can lead to significant product waste and manufacturing inefficiencies. This turns into considerable economic losses for companies, particularly in the automotive sector, where deep drawing is one of the most used cold sheet metal forming techniques. In most applications, only sample inspections are performed on batches of finished-product, with subsequent losses of time and resources. For the sake of improving the workpiece quality, innovative strategies for real-time process control represent a viable and promising solution. In this context, the proposed MPC-based process control approach allows the correct shaping of the metal sheet that is getting deformed during the forming stroke, thanks to the draw-in monitoring at various locations around the die. The draw-in is indeed one of the most effective forming variables to control in order to provide a correct BHF during the forming stroke. A useful and easy-to-implement non-linear metal sheet deep drawing process model is provided by this paper to perform an innovative process control strategy. A comprehensive methodology is applied in detail to an automotive case study, ranging from process modeling (model identification and validation based on experimental data acquisition) to MPC implementation (controller tuning and testing and software-in-the-loop system validation). The presented method can be easily implemented on any real deep drawing press, providing the multivariable constrained process with a suitable control system able to make the stamped parts well formed.

The fourth industrial revolution, also known as Industry 4.0, is reshaping the way individuals live and work while providing a substantial influence on the manufacturing scenario. The key enabling technology that has made Industry 4.0 a concrete reality is without doubtcollaborative robotics, which is also evolving as a fundamental pillar of the next revolution, the so-called Industry 5.0. The improvement of employees’ safety and well-being, together with the increase of profitability and productivity, are indeed the main goals of human-robot collaboration (HRC) in the industrial setting. The robotic controller design and the analysis of existing decision and control techniques are crucially needed to develop innovative models and state-of-the-art methodologies for a safe, ergonomic, and efficient HRC. To this aim, this paper presents an accurate review of the most recent and relevant contributions to the related literature, focusing on the control perspective. All the surveyed works are carefully selected and categorized by target (i.e., safety, ergonomics, and efficiency), and then by problem and type of control, in presence or absence of optimization. Finally, the discussion of the achieved results and the analysis of the emerging challenges in this research field are reported, highlighting the identified gaps and the promising future developments in the context of the digital evolution.Note to Practitioners—The design and development of manufacturing systems are experiencing substantial changes towards full automation. This ongoing challenge is being tackled by academia and industrial practitioners with the adoption of collaborative robots, where the skills and peculiarities of humans (e.g., intelligence, creativity, adaptability, etc.) and robots (e.g., flexibility, pinpoint accuracy, tirelessness, etc.) are combined to better perform a variety of tasks. Nevertheless, due to their different characteristics, there is an emerging need for designing suitable decision and control techniques to ensure a safe and ergonomic HRC, while keeping the highest level of productivity. Against this background, the aim of this paper is to provide researchers and practitioners with a reference source in the related field, which can help them designing and developing suitable solutions to control problems in safe, ergonomic, and efficient collaborative robotics.

This article proposes a stochastic nonlinear model predictive controller to support policymakers in determining robust optimal nonpharmaceutical strategies to tackle the COVID-19 pandemic waves. First, a time-varying SIRCQTHE epidemiological model is defined to get predictions on the pandemic dynamics. A stochastic model predictive control problem is then formulated to select the necessary control actions (i.e., restrictions on the mobility for different socioeconomic categories) to minimize the socioeconomic costs. In particular, considering the uncertainty characterizing this decision-making process, we ensure that the capacity of the healthcare system is not violated in accordance with a chance constraint approach. The effectiveness of the presented method in properly supporting the definition of diversified nonpharmaceutical strategies for tackling the COVID-19 spread is tested on the network of Italian regions using real data. The proposed approach can be easily extended to cope with other countries’ characteristics and different levels of the spatial scale.Note to Practitioners—This article is motivated by the emerging need for developing effective methods to support policymakers in mitigating the effects of the COVID-19 pandemic. The proposed feedback control strategy—combining a multiregion epidemiological model with a nonlinear stochastic model predictive control approach—allows the robust identification of the most effective restrictive measures considering the corresponding effects on the healthcare and socioeconomic systems. The proposed framework is a general and flexible method that can be applied to various real scenarios, leveraging mobility data, available from the Google mobility service, to recognize patterns and predict future behaviors of individuals.

In this paper, we address the problem of automating the definition of feasible pallets configurations. This issue is crucial for the competitiveness of logistic companies and is still one of the most difficult problems in internal logistics. In fact, it requires the fast solution of a three-dimensional Bin Packing Problem (3D-BPP) with additional logistic specifications that are fundamental in real applications. To this aim, we propose a matheuristics that, given a set of items, provides feasible pallets configurations that satisfy the practical requirements of items’ grouping by logistic features, load bearing, stability, height homogeneity, overhang as well as weight limits, and robotized layer picking. The proposed matheuristics combines a mixed integer linear programming (MILP) formulation of the 3D-Single Bin-Size BPP (3D-SBSBPP) and a layer building heuristics. In particular, the feasible pallets configurations are obtained by sequentially solving two MILP sub-problems: the first, given the set of items to be packed, aims at minimizing the unused space in each layer and thus the number of layers; the latter aims at minimizing the number of shipping bins given the set of layers obtained from the first problem. The approach is extensively tested and compared with existing approaches. For its validation we use both realistic data-sets drawn from the literature and real data-sets, obtained from an Italian logistics leader. The resulting outcomes show the effectiveness of the method in providing high-quality bin configurations in short computational times.Note to Practitioners—This work is motivated by the intention of facilitating the transition from Logistics 3.0 to Logistics 4.0 by providing an effective tool to automate bin packing, suitable for automated warehouses. On the one hand, the proposed technique provides stable and compact bin configurations in less than half a minute per bin on average, despite the high computational complexity of the 3D-SBSBPP. On the other hand, the approach allows to consider compatibility constraints for the items (e.g., final customer and category of the items), and the use of robotized layer picking in automated warehouses. In effect, layers composed by only one type of items (i.e., monoitem layers) can be directly picked and placed on the pallet by a robotic arm without the intervention of any operator. Consequently, the adoption of this approach in warehouses could drastically improve the efficiency of the packing process.

The railway sector is currently experiencing a rapid evolution from fully manual towards automatic rail traffic control systems, due to the growth of transport demand and networks complexity. One of the main issues is to automatically and effectively reschedule the railway traffic in case of unexpected events, thus avoiding dramatic drops in the system performance. In the literature, the majority of contributions aims at automatically minimizing the train delays or optimizing the railway system performance (e.g., energy consumption). However, such strategies are not always able to ensure satisfaction of passengers that in many cases experience the side-effects of the rescheduling actions (e.g., cancellation of train runs, cancellation of coincidences, rerouting of trains, etc.). In this paper, we propose a demand-oriented train rescheduling automatic technique that minimizes simultaneously the train delays and the discomfort perceived by passengers. When an unexpected event occurs, the rescheduling problem is set, based on the current state and nominal timetable of the system and its passengers flows. Hence, the problem is solved providing the control actions necessary to minimize both the delays and number of passengers subject to severe side-effects. The rescheduling is here formulated as a mixed integer linear programming problem, where the operating rules of the railway network are represented by linear equality and inequality constraints, while the objective is a linear function to be minimized. The possible control actions consist in re-timing the rail traffic and modifying the connections among lines. The proposed technique is preliminarily evaluated on a test case and a discussion is provided on the outcomes.

This article proposes a novel methodology for multistage, multiproduct, multi-item, and closed-loop Supply Chain Network (SCN) design under uncertainty. The method considers that multiple products are manufactured by the SCN, each composed by multiple items, and that some of the sold products may require repair, refurbishing, or remanufacturing activities. We solve the two main decisions that take place in the medium-/short-term planning horizon, namely partners’ selection and allocation of the received orders among them. The partners’ selection problem is solved by a cross-efficiency fuzzy Data Envelopment Analysis technique, which allows evaluating the efficiency of each SCN member and ranking them against multiple conflicting objectives under uncertain data on their performance. Then, according to the estimated customers’ demand, the order allocation problem is solved by a fuzzy bargaining game problem, where each SCN actor behaves to simultaneously maximize both its own profit and the service level of the overall SCN in terms of efficiency, costs, and lead time. An illustrative example from the literature is finally presented.Note to Practitioners—We present a decision tool to address the optimal design, performance evaluation, and continuous improvement of modern cooperative SCNs. We propose an effective method to jointly solve the members’ selection and the orders’ allocation, considering the complex structure of modern SCNs, the multiobjective nature of the problems, and the uncertainty characterizing economic markets. Competition within SCNs stages and cooperation along the chain are considered, with the aim to improve both financial and environmental sustainability, while ensuring the highest service levels to customers.

The real-time rescheduling of railway traffic in case of unexpected events is a challenging task. This is mainly due to the complexity of the railway service, which has to ensure safety, punctuality, and efficiency to customers by respecting timetable, framework, and resources constraints. Most of the available researches focus on short delays (i.e., disturbances). Approaches typically rely on simplified macroscopic models for large-scale systems or detailed microscopic models for one or a few lines, due to the long computation time required for solving the rescheduling problem. Only a small number of works consider rescheduling in case of long delays (i.e., disruptions) and all of them are also based on either a macroscopic or a microscopic model. This research focuses on disruptions and aims at filling the gap between macroscopic and microscopic modelling by proposing an innovative bi-level rescheduling algorithm based on a mesoscopic Mixed Integer Linear Programming (MILP) model. The technique allows obtaining a feasible rescheduled timetable in a short computation time respecting not only timetable and safety constraints (typical of macroscopic models) but also capacity and ordering constraints for the disrupted stations (typical of microscopic models). The bi-level algorithm first solves the macroscopic MILP rescheduling problem and then, considering the cancellation and non-admissible platform assignments results, it solves a mesoscopic MILP rescheduling problem. This allows to significantly reduce the search space and consequently the computation time. The method is tested for the rescheduling of the Dutch railway traffic in case of a full blockade between two consecutive stations.

Sheet metal forming processes are widely adopted in manufacturing industries and in the recent years there has been a growing demand for sheet metal items with different shapes and characteristics. However, the traditional process is unable to meet the modern industrial requirements, mainly due to the high costs of dies and the long manufacturing time cycles. On the contrary, developing products with high speed, low cost, and high quality is a key issue. Therefore, new methods and technologies to speed up the sheet metal forming process while keeping costs limited are needed. In particular, a key issue is the proper design of the forming process, which can benefit from the use of Information and Communications Technology (ICT) simulation techniques. This paper investigates the recent trends on ICT-based methodologies for sheet metal forming to identify the foremost research areas whose advancement will lead meeting the modern market's needs.

Model Predictive Control (MPC) has recently gained special attention to efficiently regulate Heating, Ventilation and Air Conditioning (HVAC) systems of buildings, since it explicitly allows energy savings while maintaining thermal comfort criteria. In this paper we propose a MPC algorithm for the on-line optimization of both the indoor thermal comfort and the related energy consumption of buildings. We use Fanger's Predicted Mean Vote (PMV) as thermal comfort index, while to predict the energy performance of the building, we adopt a simplified thermal model. This allows computing optimal control actions by defining and solving a tractable non-linear optimization problem that incorporates the PMV index into the MPC cost function in addition to a term accounting for energy saving. The proposed MPC approach is implemented on a building automation system deployed in an office building located at the Polytechnic of Bari (Italy). Several on-field tests are performed to assess the applicability and efficacy of the control algorithm in a real environment against classical thermal comfort control approach based on the use of thermostats.

The benefits of logistics and transportation systems to citizens, economy, and society can strongly increase when considering a smart, safe, and environmentally friendly management. This results in the implementation of intelligent transportation systems that combine innovative technologies and transportation frameworks at the aim of finding proper solutions to the related decision problems. To achieve such a goal, the intrinsic discrete event dynamics of these systems should be considered when deriving a model to be used for simulation, analysis, optimization, and control. Among the different discrete event models, Petri Nets (PNs) are particularly effective due to a series of relevant features. In addition, several high-level PN models (e.g., colored, continuous, or hybrid) allow the solution of complex and large-dimension problems that typically arise from real-life applications in the area of freight logistics and transportation systems. This paper presents a survey on contributions in this area. Papers are classified according to the addressed problem, namely, strategic/tactical or operational decision-making-level problem, and the adopted PN formalism. We also debate the approaches' viability, discussing contributions and limitations, and identify future research directions to enhance the successful application of PNs in freight logistics and transportation systems.

This paper presents a general modeling framework for Intermodal Freight Transport Terminals (IFTTs). The model allows simulating and evaluating the performance of such key elements of the intermodal transportation chain. Hence, it may be used by the decision maker to identify the IFTT bottlenecks, as well as to test different solutions to improve the IFTT dynamics. The proposed modeling framework is modular and based on timed Petri Nets (PNs), where places represent resources and capacities or conditions, transitions model inputs, flows, and activities into the terminal and tokens are intermodal transport units or the means on which they are transported. The model is able to represent the different types of existing IFTTs. Its effectiveness is tested first on an example from the literature and then on a real case study, the railroad inland terminal of a leading Italian intermodal logistics company, showing its ease of application. In the real case study, using the proposed formalism we test the as-is IFTT performance and evaluate alternative possible to-be improvements in order to identify and eliminate emerging criticalities in the terminal dynamics.

This paper focuses on modelling and performance evaluation of an Intermodal Freight Transport Terminal (IFTT), the rail-road inland terminal of a leading Italian intermodal logistics company. The IFTT is regarded as a discrete event system and is modelled in a timed Petri net framework. By means of suitable performance indices, we simulate the Petri net model and evaluate the operational performance of the transport system. This allows assessing the efficiency level of the terminal and identifying its criticalities and bottlenecks. Further, the model allows evaluating different solutions to the recognized criticalities under alternative scenarios (e.g., when inflow traffic increases and congestions may occur).