Search Results (41)

Autonomous Mobile Robots (AMRs) are increasingly important in Industry 4.0 intralogistics but creating path planning systems that adapt to dynamic and uncertain Flexible Manufacturing Systems (FMS), especially managing conflicts among multiple AMRs with a need for scalable decentralised solutions, remains a significant challenge. This research introduces a dynamic path planning system for AMRs designed for reactive adaptation to FMS disturbances and generalisation across factory layouts, incorporating support for multiple AMRs with integrated conflict avoidance. The system is built on a Multi-Agent Systems (MAS) architecture, where software AMR agents independently calculate their paths using a hybrid Genetic Algorithm (GA) that employs Cell-Based Decomposition (CBD) and optimises path length, smoothness, and overlap via a multi-objective fitness function. Multi-AMR conflict avoidance is implemented using the Iterative Exclusion Principle (IEP), which facilitates priority-based planning, knowledge sharing through Predictive Collision Avoidance (PCA), and iterative replanning among agents communicating via a blackboard agent. Verification demonstrated the system’s ability to successfully avoid deadlocks for up to nine AMRs and exhibit good scalability. Validation in a simulated FMS environment confirmed robust adaptation to various disturbances, including static and dynamic obstacles, while maintaining stable run times and consistent path quality. These results affirm the practical feasibility of this hybrid GA and MAS-based approach for dynamic AMR control in complex industrial settings. Full article

Due to the increased restrictions on antibiotic growth promoters (AGPs), natural substitutes such as organic selenium and prebiotics are gaining increased attention to enhance the gut health and performance of broilers. This study aimed at assessing the effects of organic selenium separately and in combination with mannan-oligosaccharides (MOSs) on gut development, immunity, carcass traits and overall growth performance in broiler chickens. For this study, 528-day-old straight-run broiler chicks (Ross-308) were assigned to four dietary treatments (six replicates of 22 birds each), in a completely randomized design under a 2 × 2 factorial layout, including: (1) Se0.2 (0.20 mg/kg organic selenium), (2) Se0.2 + MOS (0.20 mg/kg selenium + 1 g/kg MOS), (3) Se0.4 (0.40 mg/kg selenium) and (4) Se0.4 + MOS (0.40 mg/kg selenium + 1 g/kg MOS). The results showed that the lower level of organic selenium, along with MOS (Se0.2 + MOS), resulted in significantly enhanced (p ≤ 0.05) feed conversion ratio, body weight gain and livability. Whereas the higher level of organic selenium, along with prebiotic (Se0.4 + MOS), remarkably (p ≤ 0.05) improved carcass traits, immunity and villus height in broilers. In conclusion, the combined provision of 0.4 mg/kg organic Se and 1 g/kg MOS optimally enhances broiler performance, immunity and gut health, offering a promising substitute to AGPs. Full article

The present study implements novel innovative geostatistical imaging using precision agriculture (PA) under sugarbeet field conditions. Two driplines layout designs (d.l.d.) and soil water content (SWC)–irrigation treatments (A: d.l.d. = 1.00 m driplines spacing × 0.50 m emitters inline spacing; B: d.l.d. = 1.50 m driplines spacing × 0.50 m emitters inline spacing) were applied, with two subfactors of clay loam and clay soils (laboratory soil analysis) for modeling (evaluation of seven models) TDR multi-sensor network measurements. Different sensor calibration methods [method 1(M1) = according to factory; method 2 (M2) = according to Hook and Livingston] were applied for the geospatial two-dimensional (2D) imaging of accurate GIS maps of rootzone soil moisture profiles, soil apparent dielectric Ka profiles, and granular and hydraulic parameters profiles, in multiple soil layers (0–75 cm depth). The modeling results revealed that the best-fitted geostatistical model for soil apparent dielectric Ka was the Gaussian model, while spherical and exponential models were identified to be the most appropriate for kriging modelling, and spatial and temporal imaging was used for accurate profile SWC ${\theta v}_{TDR}$(m³·m⁻³) M1 and M2 maps using TDR sensors. The resulting PA profile map images depict the spatio-temporal soil water and apparent dielectric Ka variability at very high resolutions on a centimeter scale. The best geostatistical validation measures for the PA profile SWC ${\theta v}_{TDR}$ maps obtained were MPE = −0.00248 (m³·m⁻³), RMSE = 0.0395 (m³·m⁻³), MSPE = −0.0288, RMSSE = 2.5424, ASE = 0.0433, Nash–Sutcliffe model efficiency NSE = 0.6229, and MSDR = 0.9937. Based on the results, we recommend d.l.d. A and sensor calibration method 2 for the geospatial 2D imaging of PA GIS maps because these were found to be more accurate, with the lowest statistical and geostatistical errors, and the best validation measures for accurate profile SWC imaging were obtained for clay loam over clay soils. Visualizing sensors’ soil moisture results via geostatistical maps of rootzone profiles have practical implications that assist farmers and scientists in making informed, better and timely environmental irrigation engineering decisions, to save irrigation water, increase water use efficiency and crop production, optimize energy, reduce crop costs, and manage water resources sustainably. Full article

Near the 11–12 locks of the Canal de Castilla, there once stood a paper mill built in the 18th century and dismantled in 1983. Despite the scarce physical remains, the absence of original plans, and the limited availability of data, this research aims to reconstruct its initial layout, characteristics, and operation. To achieve this, an analytical–synthetic method has been followed: in the analysis phase, multiple sources and materials are thoroughly examined from various perspectives, while in the synthesis phase, drawing is employed as a means of reflection, since any hypothesis about this factory must be validated through sufficiently precise graphical representations. The most relevant result is the complete drafting of the factory’s plans, including its machinery, as well as the clarification of its production system. The main conclusion suggests that, although based on the mechanisms and equipment of traditional paper mills, this manufacturing facility sought to be innovative and exemplary in its time. Its dispersed typology was designed to improve and dignify working conditions for employees, while its internal organization optimized the paper production process and increased manufacturing capacity. Full article

Wind energy is a vital renewable resource with substantial economic and environmental benefits, yet its spatial variability poses significant optimization challenges. This study advances wind farm layout optimization by employing a systematic genetic algorithm (GA) tuning approach using the design of experiments (DOE) method. Specifically, a full factorial 2² DOE was utilized to optimize crossover and mutation coefficients, enhancing convergence speed and overall algorithm performance. The methodology was applied to a hypothetical wind farm with unidirectional wind flow and spatial constraints, using a fitness function that incorporates wake effects and maximizes energy production. The results demonstrated a 4.50% increase in power generation and a 4.87% improvement in fitness value compared to prior studies. Additionally, the optimized GA parameters enabled the placement of additional turbines, enhancing site utilization while maintaining cost-effectiveness. ANOVA and response surface analysis confirmed the significant interaction effects between GA parameters, highlighting the importance of systematic tuning over conventional trial-and-error approaches. This study establishes a foundation for real-world applications, including smart grid integration and adaptive renewable energy systems, by providing a robust, data-driven framework for wind farm optimization. The findings reinforce the crucial role of systematic parameter tuning in improving wind farm efficiency, energy output, and economic feasibility. Full article

The Malaysian Department of Occupational Safety and Health (DOSH) reported that noise-induced hearing loss (NIHL) accounted for 92% of occupational diseases in 2019. To address this, accurate risk assessment is crucial. The current noise evaluation methods are complex and time-consuming, relying on manual calculations and field measurements. An easy-to-use, open-source noise simulator that directly compares the output with national standards would help mitigate this issue. This research aims to develop an advanced noise evaluation tool to assess and predict unregulated workplace noise, providing tailored safety recommendations. Using a representative plant layout, the Sound Pressure Level (SPL) is calculated using MATLAB’s ray tracing propagation model. The model simulates all possible transmission paths from the source to the receiver to derive the resultant SPL. A noise simulation application featuring a graphical user interface (GUI) built with MATLAB’s App Designer (version: R2024a) automates these computations. The simulation results are validated against the DOSH’s safety standards in Malaysia. Additional safety metrics, such as the recommended maximum exposure time and the required Noise Reduction Rating (NRR) for hearing protection, are calculated based on the SPLs for hazardous locations. The simulation algorithm’s functionality is validated against manual calculations, with an average deviation of just 3.06 dB, demonstrating the model’s precision. This tool can assess and predict indoor noise levels, provide information on optimal exposure limits, and recommend necessary protective measures, ultimately reducing the risk of NIHL in factory environments. It can potentially optimise plant floor operations for existing and new facilities, ensuring safer shift operations and reducing worker noise hazard exposure. Full article

Understanding facility layout design in chemical industries requires multi-disciplinary knowledge and experience. The recent work mainly focuses on improving safety and calculating the efficiency of the design. However, in chemical industries, facilities are always located in frames, so both facility layout and frame layout should be considered in the design, as well as safety. Such a situation has not been well studied. In this work, facilities are divided into several frames and then placed in a fixed area. The risk resources located in the frames and out of the frames are both contained, and the safety distances are compliant with relative regulations. Optimization and some heuristic rules are applied to obtain the layout of each frame and the whole plant. Moreover, fire embankments are considered to achieve a more realistic and reasonable final layout. As a result, compared with the initial one, the actual and potential safety factors and the reasonable degree of the factory layout are both improved. The total costs are reduced by 7.38 × 10⁴ $·a⁻¹. Through these steps, the effectiveness of the proposed approach is proven. Full article

This paper presents a decision support approach to enable decision-makers to identify no-preference solutions in multi-objective optimization for factory layout planning. Using a set of trade-off solutions for a battery production assembly station, a decision support method is introduced to select three solutions that balance all conflicting objectives, namely, the solution closest to the ideal point, the solution furthest from the nadir point, and the one that is best performing along the ideal nadir vector. To further support decision-making, additional analyses of system performance and worker well-being metrics are integrated. This approach emphasizes balancing operational efficiency with human-centric design, aligning with human factors and ergonomics (HFE) principles and Industry 4.0–5.0. The findings demonstrate that objective decision support based on Pareto front analysis can effectively guide stakeholders in selecting optimal solutions that enhance both system performance and worker well-being. Future work could explore applying this framework with alternative multi-objective optimization algorithms. Full article

Planning and designing factory layouts are frequently performed within virtual environments, relying on inputs from various cross-disciplinary activities e.g., product development, manufacturing process planning, resource descriptions, ergonomics, and safety. The success of this process heavily relies on the expertise of the practitioners performing the task. Studies have shown that layout planning often hinges on the practitioners’ knowledge and interpretation of current rules and requirements. As there is significant variability in this knowledge and interpretation, there is a risk that decisions are made on incorrect grounds. Consequently, the layout planning process depends on individual proficiency. In alignment with Industry 4.0 and Industry 5.0 principles, there is a growing emphasis on providing practitioners involved in industrial development processes with efficient decision support tools. This paper presents a digital support function integrated into a virtual layout planning tool, developed to support practitioners in considering current rules and requirements for space claims in the layout planning process. This digital support function was evaluated by industry practitioners and stakeholders involved in the factory layout planning process. This initiative forms part of a broader strategy to provide advanced digital support to layout planners, enhancing objectivity and efficiency in the layout planning process while bridging cross-disciplinary gaps. Full article

The textile industry, a substantial component of the global economy, holds significant importance due to its environmental impacts. Particularly, the use of water and chemicals during dyeing processes raises concerns in the context of climate change and environmental sustainability. Hence, it is crucial from both environmental and economic standpoints for textile factories to adopt green industry standards, particularly in their dyeing operations. Adapting to the green industry aims to reduce water and energy consumption in textile dyeing processes, minimize waste, and decrease the carbon footprint. This approach has become crucial in achieving sustainability in textiles following the signing of the Paris Climate Agreement. Important elements of this transformation include the reuse of washing waters used in the dyeing process, the recycling of wastewater, and the enhancement of energy efficiency through necessary methodological and equipment changes. This study analyzes the energy, labor, production, and consumption data since 2011 for a textile factories with four branches located in the Adana Organized Industrial Zone. Among these factories, the one designated as UT1, which has the highest average energy and water consumption compared to the other three branches, is selected. In recent years, the use of artificial intelligence and machine learning technologies in predicting industrial processes has been increasingly observed. The data are analyzed using LSTM (Long Short-Term Memory) and ANN (Artificial Neural Networks) forecasting methods. Particularly, the LSTM algorithms, which provided the most accurate results, have enabled advanced forecasting of electricity consumption in dyeing processes for future years. In 2020, electricity consumption was recorded as 3,717,224 kWh and this consumption was reflected in the total energy cost as TRY 1,916,032. Electricity consumption accounts for 22.34% of total energy consumption, while the share of this energy type in the cost is 43.25%. In the light of these data, the MAPE value for energy consumption forecasts using the LSTM model was 0.45%, which shows that the model is able to forecast with high accuracy. As a result, a solar power plant was installed to optimize energy consumption, and in 2023 60% energy savings were achieved in summer and 25% in winter. The electricity consumption forecasting results have been an essential guide in planning strategic initiatives to enhance factory efficiency. Following improvement efforts aimed at reducing energy consumption and lowering the carbon footprint, significant optimizations in processes and layouts have been made at specific bottleneck points within the facility. These improvements have led to savings in labor, time, and space, and have reduced unit production costs. Full article

Poor layout designs in manufacturing facilities severely reduce production efficiency and increase short- and long-term costs. Analyzing and deriving efficient layouts for novel line designs or improvements to existing lines considering both the layout design and logistics flow is crucial. In this study, we performed production simulation in the design phase for factory layout optimization and used reinforcement learning to derive the optimal factory layout. To facilitate factory-wide layout design, we considered the facility layout, logistics movement paths, and the use of automated guided vehicles (AGVs). The reinforcement-learning process for optimizing each component of the layout was implemented in a multilayer manner, and the optimization results were applied to the design production simulation for verification. Moreover, a flexible simulation system was developed. Users can efficiently review and execute alternative scenarios by considering both facility and logistics layouts in the workspace. By emphasizing the redesign and reuse of the simulation model, we achieved layout optimization through an automated process and propose a flexible simulation system that can adapt to various environments through a multilayered modular approach. By adjusting weights and considering various conditions, throughput increased by 0.3%, logistics movement distance was reduced by 3.8%, and the number of AGVs required was reduced by 11%. Full article

The reasonable spatial planning of primary and secondary schools is an important factor in education development. In spatial planning, there are many models for the locations of primary and secondary schools; however, few quantitative evaluation models are available. Therefore, based on the many factors affecting the layout planning of primary and secondary schools, a knowledge graph of territorial spatial planning that considers the topological relationship, direction relationship and metric relationship in spatial planning is designed and constructed. A school location evaluation model based on the knowledge graph of territorial spatial planning is proposed. The model combines many factors of the locations of schools, such as the service population, the impact of factories on schools, the adjacency and centrality of school plots, terrain and existing schools in the region, to quantitatively evaluate whether schools are reasonably located within a region. This study focuses on the Guangyang Island area in Chongqing, China, exploring the superiority and rationality of the planned land use for primary and secondary schools within the region. By analyzing the top three and bottom three ranked schools in conjunction with the actual conditions of the site, and comparing them with AHP hierarchical analysis and ArcGIS modelling research, the study concludes that the results of this model are highly reasonable within the scope of China’s territorial spatial planning. Full article

The primary objective of this research endeavor was to devise an improved workplace design tailored to the demands of a digital factory environment. With the overarching aim of enhancing efficiency and productivity, a comprehensive proposal was formulated to improve layout configurations within the designated enterprise. The key focus lies in minimizing material transit across individual workstations, thereby mitigating potential bottlenecks and streamlining operations. Employing a structured workplace research framework, this study delved into material flow analysis techniques, augmented by the utilization of visTABLE software. While visTABLE served solely to visualize the work environment effectively, it played a crucial role in validating proposed solutions. Notably, the investigation yielded a discernible reduction in beam production time, marking a significant improvement of 10 min. These findings underscored the efficacy of the proposed solutions in addressing specific operational challenges faced by the company. Furthermore, this study facilitated a deeper understanding and visualization of the processes intrinsic to the digital factory environment. Elucidating workflow procedures at the workplace enabled stakeholders to identify areas for further improvement and refinement. In doing so, the research contributed to the overall efficiency and effectiveness of operations within the digital factory, paving the way for continued improvement and innovation in the field. Full article

Modular timber construction embodies a pioneering and eco-friendly methodology within the building sector. With the notable progress made in manufacturing technologies and the advent of engineered wood products, timber has evolved into a promising substitute for conventional materials such as concrete, masonry, and steel. Beyond its structural attributes, timber brings environmental advantages, including its inherent capacity for carbon sequestration and a reduced carbon footprint compared to conventional materials. Timber’s lightweight nature, coupled with its versatility and efficiency in factory-based production, accelerates modular construction processes, providing a sustainable solution to the growing demands of the building industry. This work thoroughly explores contemporary modular construction using wood as the primary material. The investigation spans various aspects, from the fundamentals of modularity and the classification of modular timber solutions to considerations of layout design, structural systems, and stability at both the building and module levels. Moreover, inter-module joining techniques, MEP (mechanical, electrical, and plumbing) integration, and designs for disassembly are scrutinized. The investigation led to the conclusion that timber modular construction, drawing inspiration from the steel modular concept, consistently utilizes a structural approach based on linear members (timber frame, post-and-beam, etc.), incorporating stability configurations and diverse joint techniques. Despite the emphasis on modularization and prefabrication for adaptability, a significant portion of solutions still concentrate on the on-site linear assembly process of those linear members. Regarding modularity trends, the initial prevalence of 2D and 3D systems has given way to a recent surge in the utilization of post-and-beam structures, congruent with the ascending verticality of buildings. In contrast to avant-garde and bold trends, timber structures typically manifest as rectilinear, symmetric plans, characterized by regular and repetitive extrusions, demonstrating a proclivity for centrally located cores. This work aims to offer valuable insights into the current utilization of modular timber construction while identifying pivotal gaps for exploration. The delineation of these unexplored areas seeks to enable the advancement of modular timber projects and systems, fully leveraging the benefits provided by prefabrication and modularity. Full article

Maize is an important crop to ensure food safety. High-quality seeds can guarantee a good yield. The maize seed germination rate is the most important information for the maize industry, which can be obtained through the seed germination test. An essential stage in determining the germination rate is the planting of the seeds. The current seed planting process is fully manual, which is labor-intensive and costly, and it requires the development of an autonomous seeding machine. This research developed an automatic maize seeding machine, consisting of four operations: paving sand, seed layout, watering, and covering the seed. Among the four procedures, sand paving is a crucial step, the performance of which is affected by the gate opening size, conveyor speed, and sensor mounting location. Three performance evaluating factors are the weight of sand in the tray, the volume of sand left on the conveyor, and sand surface flatness. A full factorial experiment was designed with three variables and three levels to determine an appropriate factor combination. RGB-D information was used to calculate the volume of sand left on the conveyor and sand flatness. An analytic hierarchy process was employed to assign weights to the three evaluation indicators and score the various combinations of factors. The machine for paving sand achieved a satisfactory result with an opening size of 10.8 mm, a sensor distance of 9 cm, and a conveyor belt speed of 5.1 cm/s. With the most satisfactory factors determined, the machine shows superior performance to better meet practical applications. Full article