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Nowadays, the current market trend is oriented toward increasing mass customization, meaning that modern production systems have to be able to be flexible but also highly productive. This is due to the fact that we are still living in the so-called Industry 4.0, with its cornerstone of high-productivity systems. However, there is also a migration toward Industry 5.0 that includes the human-centered design of the workplace as one of its principles. This means that the operators have to be put in the center of the design techniques in order to maximize their wellness. Among the wide set of new technologies, collaborative robots (cobots) represent one such technology that modern production systems are trying to integrate, because of their characteristic of working directly with the human operators, allowing for a mix of the flexibility of the manual systems with the productivity of the automated ones. This paper focuses on the impact that these technologies have on different levels within a production plant and on the improvement of the collaborative experience. At the workstation level, the control methodologies are investigated and developed: technologies such as computer vision and augmented reality can be applied to aid and guide the activities of the cobot, in order to obtain the following results. The first is an increase in the overall productivity generated by the reduction of idle times and safety stops and the minimization of the effort required to the operator during the work. This can be achieved through a multiobjective task allocation which aims to simultaneoulsy minimize the makespan, for productivity requirements, and the operator’s energy expenditure and mental workload, for wellness requirements. The second is a safe, human-centered, workspace in which collisions can be avoided in real time. This can be achieved by using real-time multicamera systems and skeleton tracking to constantly know where the operator is in the work cell. The system will offer the possibility of directing feedback based on the discrepancies between the physical world and the virtual models in order to dynamically reallocate the tasks to the resources if the requirements are not satisfied anymore. This allows the application of the technology to sectors that require constant process control, improving also the human–robot interaction: the human operator and the cobot are not merely two single resources working in the same cell, but they can achieve a real human–robot collaboration. In this paper, a framework is preented that allows us to reach the different aforementioned goals.

In this article, an experimental study of a wind turbine in a wind tunnel is performed. The objective has been to present a novel analytical computational fluid dynamics (CFD)-based approach through considering the residual levels of the mass and momentum parameters under effect of different air flow characteristics surrounding the wind turbine, which have an effect on the power losses, turbine’s performance and the economic viability. The involved decision variables are considered to be the wind velocity, the pressure and the turbulence. Evaluation of the convergence showed that the residual level for the maximum method is estimated to be approximately 10–1 to 10–3 times higher than the root mean square. Results also concluded that between two studied turbulence models, the turbulence eddy frequency is found to be more efficient compared with turbulence kinetic energy. In higher iterations compared with the initial iterations, a significant difference between the pressure and the Cartesian velocity components has occurred and the residual level of the velocity components indicated a more efficient convergence compared with the pressure. The overall environmental analysis concluded that on the basis of the CFD residual values, it would be possible to adequately determine the CFD efficiency of the wind energy system in a wind tunnel. It has been demonstrated that, among different decision variables, velocity components of the mass and momentum parameters and the turbulence eddy frequency were determined to produce further accurate results in comparison with the pressure and the turbulence kinetic energy.

Many assembly lines for complex products are divided into segments (zones and sections), each with its own manager. While zones are usually large and derived from the nature of the process, the segmentation into smaller sections is less obvious. This paper explores the relationship between the effects of absenteeism and turnover (requiring a slowdown because of the substitute workers’ learning period) and the segments length. The paper analyses and discusses the effect of dissecting the assembly line into sections in curbing the slowdowns due to absenteeism and turnover in large assembly lines. Quantitative model is developed to represent this factor, and bounds are found for the sections length. An important implication of dividing several hundred stations into small sections is that each section can be efficiently designed and balanced independently, making the optimization complexity irrelevant.

This research seeks to combine a comprehensive analysis of the literature regarding energy efficiency and inverters with an analysis of a configuration of a hybrid energy system including a generator, an inverter and a battery. The inverter's power output is analyzed under the influence of different distribution functions during a robust modulation procedure for achieving optimum energy saving. The modulation is tested on two processing machines independently: (a) an electric discharge machine, and (b) an injection molding machine. Based on the research methodology, problem formulations and the conducted analysis, a novel research software called “Inverter Pro V1” was programmed to analyze the estimations and performance of inverters in an energy system. An optimal levelized cost of electricity was determined given certain systems design and operating hours, and a sensitivity analysis was undertaken that identified a range of energy savings. In the sensitivity and optimization analysis, interactions among the primary decision parameters including the system's capacity, operating time, energy saving, cost of energy and payback period are investigated. It is also found that for each system, the inverter modulation categories with the optimum payback period belong to a modulation of inverter designed according to the Sawtooth function with a 45% duty cycle and switching intervals of 0.35 s. By considering the operating hours of 12.5 h per day, and the calculated system's investment cost of €31,782.158, the optimum payback period is estimated to be 1.7729 year. The study results are to inform policy with regard to assisting sustainable electricity production.

Collaborative robots (cobots) are important components of the Industry 4.0 paradigm and smart manufacturing. Cobots are known for their ability to interact with the operators in a shared workspace....

Logistics activities, e.g., transportations of goods and people, are responsible for at least one-third of energy consumption and Green House Gas (GHG) emissions. About 70% of them are related to people’s mobility, with millions of cars moving every day. The people home-work logistics represents undoubtedly an important part of it since flows are concentrated on fixed time windows (beginning, lunch break, and end of the working day) creating huge traffic congestions and negative impacts on time, economics, and the environment. This study proposes an integrated model, summarized through a methodological framework, where three actors (companies, public administrations and local shops) work together aiming to economically incentivize the use of sustainable mobility systems. Three are the main elements of the proposed sustainable people home-work logistics model: (1) the economic self-sustainability of the incentives, funded in different ways by the actors, (2) the scalability, thanks to the possibility to add new territories to the project and (3) the territorial circular economy generated thanks to the incentive’s destinations and the public-private integration. Starting from survey questionnaires and territorial attributes, sustainable mobility ways are defined. Then, participant workers are monitored by activating a mobile app, called Ecoattivi, during their home-work journeys. In such a way, workers can directly analyze their sustainable mobility and reach the possibility to accumulate and spend money in local shops as a function of the saved CO2. On the other hand, companies and public administrations compete in a special ranking for sustainable mobility. The methodological framework has been applied to a real case study in the Chiampo Valley, in the northeast of Italy, where about 10 small towns and dozens of companies in 2020 started the “Bike to Work Valchiampo” project.

Nowadays, the demand for personalized goods is increasing, with small batches of customized products. Companies are asked to cope with this need to be more competitive, thus, it is crucial to optimally set up the production process to cope with the demand. Among the products composed of several parts, sales kits occupy a significant role. Sales kits are sets of different components, supplied as a single unit. Automation is usually used to reduce the unit direct production costs for assembled products, and this is especially true for kitting since it has minimum added value, compared to other technologies. The wide range of components leads to frequent reconfiguration of the feeding devices between different products, increasing the setup time. The aim of this work is to provide a model that minimizes the setup times of automatic kitting systems when working with a high variety of products. To reach this goal, we propose to apply the traveling salesman problem (TSP) to the production process by integrating a multi-clustering model, thus increasing its appeal to several applications. This method is applied both to simulations and to a real case study, and proves to be able to provide a good solution in a reasonable time.

Assembly line balancing problems aim to an efficient and effective assignment of all the required tasks to workstations in a flow oriented production system. Nowadays, assembly lines have to face the manufacturing of extremely personalized products (e.g. cars) as requested by an increasingly higher portion of the market demand. Several literature contributions focus on different balancing problems affected by the wide variety of the final product, e.g. mixed and multi model assembly lines. However, no contribution seems to tackle the personalized production of goods. Such products require to assemble a certain number of tasks whatever the final product personalization is, and a variable number of optional of different type determined by the specifications of every single costumer. This paper faces the generalized assembly of personalized goods proposing an innovative two step methodology to optimize the workload balancing between the assembly line stations, considering traditional tasks and the optional required by the product personalization, which could occur with different frequencies and pairings. The first phase of the developed methodology executes a clustering of product options required by the costumers based on a similarity index. This phase leads to the definition of several sets of optional typically requested together by the customer and with similar mounting time. The methodology second phase leverages the defined clusters of optional. Indeed, optional of the same cluster shouldn’t be assigned to the same workstation to reduce the overload or underload of the assembly operators. An integer programming model is proposed to assign both traditional tasks and optional to stations, to maximize the assembly line balancing considering the order frequency and assembly time of the clusterized optional. An industrial case study is adopted to test and validate the proposed two steps methodology. The obtained results highlight a consistent time balancing between assembly line workstations and a significant limitation of the operator overloads.

Wind is a clean source of energy which is spread over wide globe regions. This natural source of energy encourages the planners and stakeholders establishing investments towards installation of wind farms. Wind energy experts are looking through efficient alternatives for the best utilization of the wind energy. Design of wind farms is a fundamental stage of wind energy projects. This study aims to address this issue by considering wind farm design to reduce the levelized cost of the generated wind energy. In the first part of the paper, an analysis of previous research works is carried out to find the latest advancements concerning the design of the wind farm layouts. In the next step, a real application, geo-located in Iran, investigates the effect of different layouts for the wind turbines. A cost approach based on the net present value (NPV) and the levelized cost of energy (LCOE) is used. The results show the optimum positioning of the wind turbines within the site to minimize interferences among the blades maximizing the economic return on the investment.

The advances in Industry 4.0 provide both challenges and opportunities for digital manufacturing and assembly systems. This paper first addresses the state-of-the-art readiness for Industry 4.0 concerning assembly and manufacturing systems through a literature review of the relevant papers recently published. Then it assesses the challenges faced nowadays by assembly and manufacturing systems. Third, it focuses on the most promising future developments and evolution of such production systems as well as their digitalisation. Finally, this manuscript illustrates the content of the papers selected for this special issue. Through the study presented in this special issue, valuable contributions to both theory and application in this area have been achieved, and a useful reference for future research is given.

Purpose The current study aims to propose a new analytical approach by considering energy consumption (EC), maximum tardiness and completion time as the primary objective functions to assess the performance of parallel, non-bottleneck and multitasking machines operating in dynamic job shops. Design/methodology/approach An analytical and iterative method is presented to optimize a novel dynamic job shop under technical constraints. The machine’s performance is analyzed by considering the setup energy. An optimization model from initial processing until scheduling and planning is proposed, and data sets consisting of design parameters are fed into the model. Findings Significant variations of EC and tardiness are observed. The minimum EC was calculated to be 141.5 hp.s when the defined decision variables were constantly increasing. Analysis of the optimum completion time has shown that among all studied methods, first come first served (FCFS), earliest due date (EDD) and shortest processing time (SPT) have resulted in the least completion time with a value of 20 s. Originality/value Considerable amount of energy can be dissipated when parallel, non-bottleneck and multitasking machines operate in lower-power modes. Additionally, in a dynamic job shop, adjusting the trend and arrangement of decision variables plays a crucial role in enhancing the system’s reliability. Such issues have never caught the attention of scientists for addressing the aforementioned problems. Therefore, with these underlying goals, this paper presents a new approach for evaluating and optimizing the system’s performance, considering different objective functions and technical constraints.

The novel generation of production facilities fostered by the fourth industrial revolution widely adopts different technologies to digitalise the manufacturing and assembly processes. In this context, work measurement techniques are one of the main candidates for the application of these new technologies because of the time, cost, and competences required to analyse manual production activities and considering the limited precision of the traditional approaches. This paper proposes a new hardware/software architecture devoted to the motion and time analysis of the activities performed by human operators within whatsoever industrial workplace. This architecture, called Human Factor Analyser (HFA), is constituted by a network of ad hoc depth cameras able to track the worker movements during the task execution without any interference with the monitored process. The data provided by these cameras are then elaborated in a post-process phase by the HFA to automatically and quantitatively measure the work content of the considered activities through an accurate motion and time analysis. The developed architecture evaluates the worker in a 3D environment considering his interaction with the industrial workplace through the definition of appropriate control volumes within the layout. To test the accuracy of HFA, an extensive experimental campaign is performed at the Bologna University Laboratory for Industrial Production adopting several realistic industrial configurations (different workplaces, operators and tasks). Finally, the HFA is applied to a real manufacturing case study of an Italian company producing refrigerator metal grates. A wide and deep analysis of the obtained key results is presented and discussed.

Recently, the need for assembly systems of better following the market demand has led to the spread of alternative solutions with respect to the traditional fixed worker strategy. Walking worker systems, where operators move across the line to produce the finished product, are gaining always more interest. In this paper, a comparison between the fixed worker (FW) and the walking worker (WW) assembly strategies is provided, by taking into account their differences both in terms of performed activities during assembly and of material exposure at the assembly workstations. The evaluation is carried out through the proposal of a mathematical model, to understand the impact on assembly time and on operators’ energy expenditure. The behavior of the model is investigated both with a parametric analysis and a real case study. The results show that the FW is the best one when the system is working at its maximum throughput, while the WW strategy turns out to be preferable for the lower values of market demand, both from a time and an energy expenditure perspective.

Smart assembly lines are a milestone of Industry 4.0 allowing the efficient and effective production of complex products with an acceptable time-to-market. According to the literature and the standard practice, these lines are made of a set of equipped stations with a worker for each of them. Products flow the stations in sequence to perform their assembly cycle. Nowadays, the best design of assembly lines deals with the joint inclusion and best mix of multiple dimensions, often in conflict and requiring good trade-off solutions. This paper proposes and preliminary applies a tri-objective linear programming model to design a single-model smart assembly line aiming at optimizing the system annual equivalent cost, the time balance among the stations and, finally, the level of fatigue each worker is exposed. Details on the model boundaries are given together with its full mathematical formulation and linearization, to reduce its complexity. Finally, a preliminary case study links the model to practice showing its feasibility and potential benefit for the modern smart industries.

Industry 4.0 emerged in the last decade as the fourth industrial revolution aiming at reaching greater productivity, digitalization and operational efficiency standard. In this new era, if compared to automated assembly systems, manual assembly systems (MASs) are still characterized by wide flexibility but poor productivity levels. To reach acceptable performances in terms of both productivity and flexibility, higher automation levels are required to increase the skills and capabilities of the human operators with the aim to design next-generation assembly systems having higher levels of adaptivity and collaboration between people and automation/information technology. In the current literature, such systems are called adaptive automation assembly systems (A3Ss). For A3Ss, few design approaches and industrial prototypes are available. This paper, extending a previous contribution by the Authors, expands the lacking research in the field and proposes a general framework guiding toward A3S effective design and validation. The framework is applied to a full-scale prototype, highlighting its features together with the technical- and human-oriented improvements arising from its adoption. Specifically, evidence from this study show a set of benefits from adopting innovative A3Ss in terms of reduction of the assembly cycle time (about 30%) with a consequent increase of the system productivity (about 45%) as well as relevant improvements of ergonomic posture indicators (about 15%). The definition of a general framework for A3S design and validation and the integration of the productivity and ergonomic analysis of such systems are missing in the current literature, representing an element of innovation. Globally, this research paper provides advanced knowledge to guide research, industrial companies and practitioners in switching from traditional to advanced assembly systems in the emerging Industry 4.0 era matching current industrial and market features.

Collaborative robots represent an interesting solution to automate tasks that were too difficult or too expensive to automate since they merge the flexibility of a human operator and the accuracy of automated systems. This work presents a framework developed in three levels with a top-down approach. A first offline level will solve the task scheduling problem of a human-robot collaborative workcell addressing both logistic and robotic aspects, represented by physical interferences and performance indexes. A second level will manage the shared workspace in real-time. These first two levels, due to their general nature, will be described in greater detail. The possible application and validation of the model to a specific case study will be discussed in the third level, which will describe the connection of the model towards different devices.

During the last decade the use of digital twins has been spreading to various complex systems, such as assembly lines, and complex products such as aircraft, drones, satellites, vehicles and machinery. This paper discusses the considerations related to the interaction between the assembly-line digital twin and the digital twins of the evolving complex products (workpieces). The paper proposes hierarchical framework including the assembly-line digital twin at the top, that employs the workstation digital twins which are temporarily connected to their current workpiece digital twins. The result is a system that can accurately and efficiently synchronize these simultaneous digital twins. A shared data structure is proposed to facilitate the control and tracking of the assembly progress of each workpiece. This data structure is carried with the workpiece and is shared with the current station digital twin and if necessary the line digital twin.

Collaborative robots can be a proper solution to improve the throughput of manual systems without reducing their flexibility. To effectively use cobots in productive systems it is fundamental to develop a suitable task allocation model that considers collaboration. Hence, we present a model for collaborative assembly line balancing (C-ALB) which considers paralleling tasks and collaboration in the balancing resolution. Indexes that take into account both the product and process characteristics are defined to evaluate the quality of the proposed task allocation model and comparing it to others. The results confirm the influence of the product characteristics on the system performance, leading to the definition of a new paradigm for product design.

The Walking Workers (WW) systems are interesting for their flexibility attributes, especially in terms of ability to manage the product variety and market demand. This paper aims to study the mixed model sequencing problem considering un-paced, unbuffered WW systems. The authors propose an original algorithm, called “accordion algorithm” for the models sequence definition and the makespan minimization. This algorithm is tested and validated through a simulative study highlighting the influence of the task time variations and of the resources number on the system performances.

The assembly of large and complex products as cars typically involves a huge amount of production resources as workers, pieces of equipment and layout areas. Multi-manned workstations commonly characterize these assembly lines along with some additional features such as compatibility/incompatibility between the different mounting positions, equipment sharing and worker cooperation. The simultaneous management of all such aspects significantly increases the balancing problem complexity. This paper proposes a new mixed integer programming model for such multi-manned assembly line synchronization problem to simultaneously optimize the line efficiency, the line length and the workload smoothness. A customized procedure based on a simulated annealing algorithm is developed to effectively solve this problem. The aforementioned procedure is validated through an industrial case study of an Italian sports car manufacturer distinguished by 665 tasks and numerous synchronization constraints typical of real assembly lines.

Collaborative robots (cobots) are intended to physically interact with humans in a shared workspace. While cobots research proliferated in the recent decade, only scant attention was given to the strategic consideration of deploying them. The obvious strategic consideration is related to economic cost-benefits trade-off. However, the economic decision is tightly tied to the technology improvement rate, as cobots lifetime expectancy strongly depend on the technological developments. In this regard, the difference between different types of cobots may be dramatic. Another strategic issue is the sociological effects including the reaction of the operators and unions to cobot deployment. This paper reviews the related literature and proposes a model to analyze the underlying factors and facilitate the decision making process of: where and when to deploy which cobots.

This paper aims to present a methodology and multi-scenario application for the techno-economic assessment of the wind turbine layout in a wind farm. The considered decisional variables deal with the number of wind turbines to install, their self-distance and their distance toward the primary grid line. In addition, the wind speed profile and the site boundary environmental conditions are within the methodology. The levelized cost of energy (LCOE) and the net present value (NPV) are estimated as the key results and decisional indicators to compare multiple scenarios within two relevant locations in the Middle East (a case study of Abadan site in Iran) and the Mediterranean region (a case study of Swatar site in Malta). Finally, for each site, an effective layout of the wind farm is selected. Numerical results lead to the optimum layout with the lowest cost of energy. Swatar site has the highest potential of wind for the energy production. The LCOE for this site is estimated to be 4.01 c€/kWh for a wind farm with a size of 10 MW and 5.76 c€/kWh for a wind farm with size of 25 MW. Overall, it is shown that the wind power fosters sustainable development in both case studies and the proposed structures and layouts increase the performances of the wind farms, considerably.