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The assembly of large and complex products such as cars, trucks, and white goods typically involves a huge amount of production resources such as workers, pieces of equipment, and layout areas. In this context, multi-manned workstations commonly characterize these assembly lines. The simultaneous operators’ activity in the same assembly station suggests considering compatibility/incompatibility between the different mounting positions, equipment sharing, and worker cooperation. The management of all these aspects significantly increases the balancing problem complexity due to the determination of the start/end times of each task. This paper proposes a new mixed-integer programming model 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 applied to the balancing of the real assembly line of European sports car manufacturers distinguished by 665 tasks and numerous synchronization constraints. The experimental results present remarkable performances obtained by the proposed procedure both in terms of solution quality and computation time. The proposed approach is the practical reference for efficient multi-manned assembly line design, task assignment, equipment allocation, and mounting position management in the considered industrial fields.

Capitalizing on the widespread adoption of smartphones among farmers and the application of artificial intelligence in computer vision, a variety of mobile applications have recently emerged in the agricultural domain. This paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region. Developed through a co-design methodology involving direct collaboration with Italian farmers, this participatory approach resulted in an app featuring: (i) a graphical interface optimized for diverse in-field lighting conditions, (ii) a user-friendly interface allowing swift selection from a predefined menu, (iii) operability even in low or no connectivity, (iv) a straightforward operational guide, and (v) the ability to specify an area of interest in the photo for targeted threat identification. Underpinning GranoScan is a deep learning architecture named efficient minimal adaptive ensembling that was used to obtain accurate and robust artificial intelligence models. The method is based on an ensembling strategy that uses as core models two instances of the EfficientNet-b0 architecture, selected through the weighted F1-score. In this phase a very good precision is reached with peaks of 100% for pests, as well as in leaf damage and root disease tasks, and in some classes of spike and stem disease tasks. For weeds in the post-germination phase, the precision values range between 80% and 100%, while 100% is reached in all the classes for pre-flowering weeds, except one. Regarding recognition accuracy towards end-users in-field photos, GranoScan achieved good performances, with a mean accuracy of 77% and 95% for leaf diseases and for spike, stem and root diseases, respectively. Pests gained an accuracy of up to 94%, while for weeds the app shows a great ability (100% accuracy) in recognizing whether the target weed is a dicot or monocot and 60% accuracy for distinguishing species in both the post-germination and pre-flowering stage. Our precision and accuracy results conform to or outperform those of other studies deploying artificial intelligence models on mobile devices, confirming that GranoScan is a valuable tool also in challenging outdoor conditions.

Mass customization (MC) has become a pivotal manufacturing strategy for addressing the growing demand for personalized products without compromising cost efficiency and scalability. The emergence of Industry 4.0 (I4.0) has further expanded the potential of MC by enabling intelligent, flexible, and interconnected production systems. This paper presents a systematic literature review covering the period from 2011 to 2024, aimed at examining how I4.0 technologies influenced the conceptual evolution, technological enablers, and supply chain implications of MC. A total of 3441 publications were retrieved from Scopus and analyzed using a combination of bibliometric mapping and qualitative synthesis. The review identifies three primary research streams: (1) MC conceptual frameworks and performance metrics, (2) enabling technologies and methods across the product lifecycle, and (3) supply chain strategies tailored to MC environments. Key enablers such as product modularity, customer co-design platforms, additive manufacturing, and reconfigurable production systems are discussed, along with barriers related to complexity, integration challenges, and sustainability trade-offs. The study highlights a gradual convergence toward mass personalization, supported by real-time data, artificial intelligence, and predictive analytics. The findings offer a structured understanding of MC in the I4.0 context and point toward future research opportunities involving digital twin integration, cross-disciplinary implementation models, and sustainability-driven customization frameworks.

Nowadays, end customers require personalized products to match their specific needs. Thus, production systems must be extremely flexible. Companies typically exploit assembly lines to manufacture produces in great volumes. The development of assembly lines distinguished by mixed or multi models increases their flexibility concerning the number of product variants able to be manufactured. However, few scientific contributions deal with customizable products, i.e., produces which can be designed and ordered requiring or not a large set of available accessories. This manuscript proposes an original two-step procedure to deal with the multi-manned assembly lines for customized product manufacturing. The first step of the procedure groups the accessories together in clusters according to a specific similarity index. The accessories belonging to a cluster are typically requested together by customers and necessitate a significant mounting time. Thus, this procedure aims to split accessories belonging to the same cluster to different assembly operators avoiding their overloads. The second procedure step consists of an innovative optimization model which defines tasks and accessory assignment to operators. Furthermore, the developed model defines the activity time schedule in compliance with the task precedencies maximizing the operator workload balance. An industrial case study is adopted to test and validate the proposed procedure. The obtained results suggest superior balancing of such assembly lines, with an average worker utilization rate greater than 90%. Furthermore, in the worst case scenario in terms of customer accessories requirement, just 4 line operators out of 16 are distinguished by a maximum workload greater than the cycle time.

PurposeThird-party logistic providers (3PLs) continuously strive for controlling and improving their performances to gain a competitive advantage. The challenging environment where they operate is affected by high variety in type and number of clients, the inventory mix and the demand profiles they have to meet. Consequently, better understanding the dynamics of warehousing operations and the characteristics of the inventory mix is critical to handle such a complexity.Design/Methodology/approachThis paper proposes a decision-support framework, suited for 3PL warehouse practitioners, that aids to design and implement effective and affordable activities for measuring and improving the warehousing performances. Such goal is pursued by the framework by leading the managers through an initial mapping and diagnosis of the system, then by developing a tailored measurement system to track the performance, paving the way to the identification of the criticalities and the potential improvement scenarios.FindingsThis paper presents a case study on the implementation of the proposed framework at a warehouse of an Italian 3PL provider to introduce a new storage assignment policy and reduce the travelling time for order picking. Furthermore, the paper exemplifies how the framework contributes to enhance the awareness of managers on warehousing operations and the involvement of the personnel throughout the improvement process.Practical implicationThe proposed framework can be implemented by operations managers of 3PL warehouses who want to pursue general performance improvement projects. With respect to the case study, this framework contributes to identify the storage assignment policy that reduces the travelling for order picking in the observed warehouse of 8 percent in a month but is intended to address to even other areas of improvement in 3PL warehousing environments.Originality/valueInstead of focusing on the proper methods and models that optimize a specific task or performance indicator, it provides a general framework that leads the managers through the decisional process, from the preliminary diagnosis of the system, to its benchmarking, towards the implementation of corrective and improving solutions.

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.

Background There has been a growing interest in pathologic spine-hip relations (PSHR) in current literature, with the aim of reducing the risk of prosthetic impingement, dislocation, and edge loading in total hip arthroplasty (THA). The primary objective of this review is to determine the effect of different PSHR on primary THA outcomes and complication profile. The secondary objective is to stratify the risk of different subgroups of PSHR patients. Methods A systematic review of the literature was performed in accordance with PRISMA guidelines. Randomised controlled trials, comparative cohort studies and case–control studies comparing outcomes and complication rates of primary THA in patients with and without a PSHR (spinal fusion; degenerative spinal conditions determining stiff spine and/or spinal misalignment) were included. The quality of the included studies and the risk of bias were assessed. The revision rate, complications, and clinical and radiological data were analysed. Complications included: aseptic loosening (AL), periprosthetic joint infections (PJI), hip dislocations and periprosthetic fractures (PF). Results Fifteen articles were included with 3.306.342 THAs. The mean follow-up (FU) was 31.4 ± 21.7 months. The population was divided into three subgroups: spinal fusion patients (48.315 THAs); non-fused patients with spinal stiffness (106.110 THAs); non-fused patients with normal spines (3.151.917 THAs). A statistically significant risk stratification was observed about dislocation rate (5.98 ± 6.9% SF, 3.0 ± 1.9% non-SF Stiff and 2.26 ± 1.4% non-SF; p  = 0.028). Similarly, about THA revision rate, a statistically significant risk stratification was also observed (7.3 ± 6.8% SF, 6.4 ± 3.1% non-SF Stiff and 2.7 ± 1.7% non-SF; p  = 0.020). No statistically significant difference was observed when analysing AL, PJI and PF. Conclusion A statistically significant risk stratification of dislocation and revision rate was observed in the different PHSR, as theorised by the Bordeaux classification. Fused patients present a higher risk, degenerated and/or stiff spine an intermediate risk and mobile spines a lower risk profile. A standardised approach to THA candidate patients must consider the possible PSHR to improve clinical outcomes and reduce adverse events of THA.

Non-traditional warehouses shorten the travelled paths to store and retrieve (S/R) the loads, thanks to additional aisles crossing the parallel racks. This paper provides the analytic model to best design a non-traditional warehouse for unit-load (UL) with diagonal cross-aisles and storage policy according to the class-based storage (CBS) strategy. The model minimizes the average single-command cycle time to S/R the loads, best sizing the classes, their shape, and the position/numbers of additional aisles. The focus is on both 2- and 3-CBS optimizing the number of diagonal cross-aisles to best balance the travel time reduction and the loss of storage space due to the aisles. Furthermore, benchmarking toward standard warehouses with no diagonal cross-aisles and random assignment strategy allows quantifying the positive impact of the proposed design configuration on the daily warehouse operations.

Organic waste (OW) management tackles the problem of sanitation and hygiene in developing countries and humanitarian camps where unmanaged waste often causes severe health problems and premature death. OW still has a usable energy content, allowing biogas production, potentially contributing to satisfy the local needs, e.g., cooking, lighting and heating. Digesters are the devices converting OW into biogas under anaerobic conditions. They are simple and effective solutions for the OW management in rural areas, humanitarian camps and remote developing regions, producing energy and fertilizers for local farming as residual. This paper describes the design and lab-test of a domestic OW management system integrating a waterless toilet with a small-scale digester producing safe biogas for local micro-consumption. Starting from people’s needs and an extensive review of the current state-of-art technology, the proposed system’s key innovation and strength is the combination of effectiveness and a very simple construction, set up and use, fitting with the rural conditions and raw materials available within the emerging countries. A small-scale prototype is assembled and lab-tested assessing the quantity—i.e., productivity—and quality—i.e., composition and methane content—of the produced biogas. The measured productivity in terms of specific biogas production (SBP) is of about 0.15 m3/kgSV and a methane content of about 74% in mass match the energy needs of domestic users, encouraging the spread of such systems in developing regions and rural areas.

PurposeThe food processing industry is growing with retail and catering supply chains. With the rising complexity of food products and the need to address food customization expectations, food processing systems are progressively shifting from production line to job-shops that are characterized by high flexibility and high complexity. A food job-shop system processes multiple items (i.e. raw ingredients, toppings, dressings) according to their working cycles in a typical resource and capacity constrained environment. Given the complexity of such systems, there are divergent goals of process cost optimization and of food quality and safety preservation. These goals deserve integration at both an operational and a strategic decisional perspective. The twofold purpose of this paper is to design a simulation model for food job-shop processing and to build understanding of the extant relationships between food flows and processing equipment through a real case study from the catering industry.Design/methodology/approachThe authors designed a simulation tool enabling the analysis of food job-shop processing systems. A methodology based on discrete event simulation is developed to study the dynamics and behaviour of the processing systems according to an event-driven approach. The proposed conceptual model builds upon a comprehensive set of variables and key performance indicators (KPIs) that describe and measure the dynamics of the food job-shop according to a multi-disciplinary perspective.FindingsThis simulation identifies the job-shop bottlenecks and investigates the utilization of the working centres and product queuing through the system. This approach helps to characterize how costs are allocated in a flow-driven approach and identifies the trade-off between investments in equipment and operative costs.Originality/valueThe primary purpose of the proposed model relies on the definition of standard resources and operating patterns that can meet the behaviour of a wide variety of food processing equipment and tasks, thereby addressing the complexity of a food job-shop. The proposed methodology enables the integration of strategic and operative decisions between several company departments. The KPIs enable identification of the benchmark system, tracking the system performance via multi-scenario what-if simulations, and suggesting improvements through short-term (e.g. tasks scheduling, dispatching rules), mid-term (e.g. recipes review), or long-term (e.g. re-layout, working centres number) levers.