Search Results (93)

Globalization has greatly changed the way logistics firms function, improving speed, accuracy, and efficiency in everything from logistic management to warehousing. Robotics and automation technologies driven by artificial intelligence improve warehouse operations’ efficiency and adaptability, allowing warehouses to easily manage a variety of items, packaging kinds, and order profiles. Nevertheless, more research is still needed to fully comprehend how automation has affected logistics and how it has evolved. In addition, to date, no scholarly work has provided a thorough analysis of particular automated logistic process automation strategies used by Lithuanian businesses. Although many of the assessments that are currently available in this field offer valuable insights, they are frequently overly broad. In order to tackle this problem, we conducted a methodical study that attempts to offer a strong and pertinent basis, focusing on the automation of logistics processes that are used in supply chain management together with artificial intelligence. This study’s objective was to examine conditions for increasing logistics automation processes in Lithuanian logistic companies. The novelty of this article is the consideration of the main factors influencing the automation of logistics processes, which include the key drivers of AI-powered warehouse automation processes to evaluate the real level of automation. Full article

Warehouse 4.0 adopts automation, IoT, and big data technologies to establish an intelligent warehousing system for efficient, real-time management of storage, handling, and picking. Addressing challenges like unreasonable storage allocation and inefficient order fulfillment, this paper presents an integrated framework that utilizes swarm intelligence algorithms and collaborative scheduling strategies to optimize inbound/outbound operations. First, for inbound processes, an algorithm-driven storage allocation model is proposed to solve stacker crane scheduling problems. Then, for outbound operations, a “1+N+M” mathematical model is developed, optimized through a three-stage algorithm addressing order picking and distribution scheduling. Finally, a case study of an industrial warehouse validates the proposed methods. The improved mayfly algorithm demonstrates excellent performance, achieving 64.5–74.5% faster convergence and 20.1–24.7% lower fitness values compared to traditional algorithms. The three-stage approach reduces order fulfillment time by 12% and average processing time by 1.8% versus conventional methods. These results confirm the framework’s effectiveness in enhancing warehouse operational efficiency through intelligent automation and optimized resource scheduling. Full article

5G private mobile networks are becoming a platform for ‘wire-free’ networking for professional applications in smart industry sectors, such as automated warehousing, logistics, autonomous vehicle deployments in campus environments, mining, material processing, and more. It is expected that most of these Machine-to-Machine (M2M) and Industrial Internet of Things (IIoT) communication paths will be realized wirelessly, as the advantages of providing flexibility are obvious compared to hard-wired network installations. Unfortunately, the deployment of private 5G networks in smart industries has faced delays due to a combination of high costs, technical challenges, and uncertain returns on investment, which is reflected in troublesome access to fully operational private networks. To obtain insight into occupational exposure to radiofrequency electromagnetic fields (RF EMF) emitted by 5G private mobile networks, an analysis of RF EMF due to different types of 5G equipment was carried out on a real case scenario in the production and logistic (warehouse) industrial sector. A private standalone (SA) 5G network operating at 3.7 GHz in a real industrial environment was numerically modeled and compared with in situ RF EMF measurements. The results show that RF EMF exposure of the workers was far below the existing exposure limits due to the relatively low power (1 W) of indoor 5G base stations in private networks, and thus similar exposure scenarios could also be expected in other deployed 5G networks. In the analyzed RF EMF exposure scenarios, the radio transmitter—so-called ‘radio head’—installation heights were relatively low, and thus the obtained results represent the worst-case scenarios of the workers’ exposure that are to be expected due to private 5G networks in smart industries. Full article

The concepts of sustainable development, triple bottom line, and ESG have a strong influence on the process of formation and operation of supply chains today. This requires the implementation of various green solutions and practices to improve supply chain sustainability. An analysis of supply chain research did not reveal a universally accepted methodology to systematize green solutions and practices for their effective use in chain management. It was revealed that there are many views on the content of green solutions, in addition to insufficient specificity of their description, as well as fragmentation of the use of green solutions in relation to the elements and functions of supply chains (procurement, production, warehousing, transportation, and distribution). This reduces the effectiveness of the implementation of green solutions. In this study, based on the literature review, a systematization of currently existing green solutions and practices was carried out. The systematization was performed according to the affiliation of supply chain elements and the functions performed by the elements to promote and process the material flow from supplier to consumer. The proposed system of methods (GLMs) and instruments (GLIs) of green logistics covers all known functional areas of logistics and includes 27 methods and 105 instruments. We performed a ranking of methods and instruments using TOPSIS, MABAC, and MARCOS methods. The most and least significant GLM and GLI for each element of the supply chain, as well as for chains of complex structure in general, were determined. The results of GLM and GLI ranking can be used as a basis for the implementation of management decisions to improve the sustainability of supply chains. Full article

With the rapid development of e-commerce, warehousing and logistics systems are facing the dual challenges of increasing order processing demand and green and low-carbon transformation. Traditional manual and single-robot scheduling methods are not only limited in efficiency, but will also make it difficult to meet the strategic needs of sustainable development due to their high energy consumption and resource redundancy. Therefore, in order to respond to the sustainable development goals of green logistics and resource optimization, this paper replaces the traditional mobile handling robot in warehousing and logistics with a composite robot composed of a mobile chassis and a robotic arm, which reduces energy consumption and labor costs by reducing manual intervention and improving the level of automation. Based on the traditional contract net protocol framework, a distributed task allocation strategy optimization method based on an improved genetic algorithm is proposed. This framework achieves real-time optimization of the robot task list and enhances the rationality of the task allocation strategy. By combining the improved genetic algorithm with the contract net protocol, multi-robot multi-task allocation is realized. The experimental results show that the improvement strategy can effectively support the transformation of the warehousing and logistics system to a low-carbon and intelligent sustainable development mode while improving the rationality of task allocation. Full article

In the four-way shuttle system, the efficiency of path planning directly affects the overall effectiveness of logistics and warehousing operations. Traditional path planning methods for multiple four-way shuttles do not take into account the fact that the map status will change as the inbound and outbound tasks are completed. To address this issue, a path planning algorithm for dynamic environments based on an improved Conflict-Based Search (CBS) mechanism is proposed. Firstly, by introducing turning constraints and a node expansion strategy, the A* algorithm is improved, reducing the number of turns and optimizing the node expansion process. Secondly, based on the improved A* algorithm, a path planning algorithm for dynamic environments based on CBS is designed. This algorithm adopts the inbound/outbound task priority strategy and the nearby-task priority strategy to resolve conflicts. It effectively manages the changes in the map status by establishing and maintaining a “ChangeList” and revises the path set of the four-way shuttles. Based on the layout of the intelligent vertical warehouse with four-way shuttles of a certain enterprise, simulation experiments were carried out using a rasterized map. The algorithm was compared with the DCBS-PFM and RRT-A algorithms, verifying the effectiveness and superiority of the algorithm. Full article

In human–robot hybrid intelligent warehouses, pallets often come in various shapes and sizes, posing challenges for AGVs to automate pallet picking. This, in turn, reduces the overall operational efficiency of the warehouse. To address this issue, this paper proposes a lightweight component segmentation network using a dual-attention mechanism to achieve precise segmentation of the pallet’s stringer board and accurate localization of the pallet slots. To overcome the challenge of redundant computations in existing semantic segmentation models, which are unable to balance spatial details and high-level semantic information, this network utilizes a dual-branch attention mechanism within an encoder–decoder architecture to effectively capture spatial details. On this basis, a residual structure is introduced to reduce redundant network parameters, addressing issues like vanishing and exploding gradients during training. Due to the lack of a public pallet image segmentation dataset, the network was tested using a custom-made dataset. The results show that by extracting intermediate-, low-, and high-level features from dual-branch input images and integrating them to construct multi-scale images, precise segmentation of various types of pallets can be achieved with limited annotated images. Furthermore, to comprehensively evaluate the model’s robustness, additional pallet localization experiments were conducted under varying illumination conditions and background noise levels. The results demonstrate that the proposed method can effectively identify and locate multi-category pallet targets while maintaining high segmentation accuracy under different lighting conditions and background interferences, verifying the model’s robustness in complex warehousing environments. Compared to the traditional model, the proposed model in this paper achieves a 10.41% improvement in accuracy and a 32.8% increase in image processing speed. The segmentation network we proposed is used for pallet-positioning experiments and has achieved good positioning results in pallet images taken from different distances and angles. Full article

Barcode technology plays a crucial role in automatic identification and data acquisition systems, with extensive applications in retail, warehousing, healthcare, and industrial automation. However, barcode images often suffer from blurriness due to lighting conditions, camera quality, motion blur, and noise, adversely affecting their readability and system performance. This paper proposes a multi-scale real-time deblurring method based on edge feature guidance. Our designed multi-scale deblurring network integrates an edge feature fusion module (EFFM) to restore image edges better. Additionally, we introduce a feature filtering mechanism (FFM), which effectively suppresses noise interference by precisely filtering and enhancing critical signal features. Moreover, by incorporating wavelet reconstruction loss, the method significantly improves the restoration of details and textures. Extensive experiments on various barcode datasets demonstrate that our method significantly enhances barcode clarity and scanning accuracy, especially in noisy environments. Furthermore, our algorithm ensures robustness and real-time performance. The research results indicate that our method holds significant promise for enhancing barcode image processing, with potential applications in retail, logistics, inventory management, and industrial automation. Full article

To facilitate the automation of crane operations for grabbing coil stacks in port storage areas, thereby streamlining the processes of warehousing, stacking, and transshipment for enhanced operational efficiency, this paper utilizes algorithms related to 3D point clouds for the pose estimation of coil workpieces. To overcome the limitations of the traditional point pair feature (PPF) algorithm, a novel point cloud registration algorithm is introduced. This algorithm harnesses the advantages of the PPF algorithm in describing local features and integrates it with the Generalized Iterative Closest Point (GICP) algorithm to enhance the robustness and applicability of registration. Finally, comparative experiments demonstrate that the proposed algorithm delivers superior performance. The average pose estimation errors for one, two, and three coils are 1.1%, 1.1%, and 1.2% of the coil size, respectively, with total processing times of 3.6 s, 3.4 s, and 4.7 s, meeting the practical application requirements in terms of accuracy and timing. Full article

Quality risk management, commonly known as QRM, is designed to systematically assess, control, communicate, and review potential risks at every stage of the pharmaceutical manufacturing process. The preservation of consistent product quality across the entirety of the product’s life cycle is of paramount importance. The aim of this article is to formulate a best practice guide that will assist pharmaceutical manufacturers in comprehending and implementing the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Q9: quality risk management principles. A widely recognized methodology for defining and monitoring risk mitigation strategies within the pharmaceutical sector is the Failure Mode and Effects Analysis (FMEA). ICH Q9 does not, however, offer detailed instructions for applying FMEA to real-world pharmaceutical situations. We previously provided real-world case studies that identify and mitigate risks in the early stages of the manufacturing process of sterile products, such as (1) supply chain and procurement; (2) logistics and warehousing; (3) raw material dispensing; (4) glass bottle washing and handling; (5) product filling; and (6) final product receiving and handling. The final steps of the sterile manufacturing process are the subject of the case study we present in this paper. We identify and control the risks related to (I) product sterilization; (II) product inspection, labeling, and packaging; (III) the finished product’s transfer to storage; and (IV) storing finished products in a warehouse. In order to maximize decision-making and reduce the risk of regulatory noncompliance, this case study describes a proactive strategy for the identification, management, and communication of risks associated with crucial tasks. While each organization’s products and methods are distinct, with varying tolerances for risk, certain stages and associated risks are common. Consequently, the examples provided here offer relevant insights into any pharmaceutical production environment. Managing sustainability-related risks and ensuring the transparency of pharmaceutical company operations are key tasks of success today. These risks, if not managed, will cause serious problems and a negative reputation, as well as environmental and public impact. Full article

The complex multi-stage process of meat processing encompasses critical phases, including slaughtering, cooling, cutting, packaging, warehousing, and logistics. The quality and nutritional value of the final meat product are significantly influenced by each processing link. To address the major challenges in the meat processing industry, including device heterogeneity, model deficiencies, rapidly increasing demands for data analysis, and limitations of cloud computing, this study proposes an Internet of Things (IoT) architecture. This architecture is centered around an intelligently decoupled gateway design and edge-cloud collaborative intelligent meat inspection. Pork freshness detection is used as an example. In this paper, a high-precision and lightweight pork freshness detection model is developed by optimizing the MobileNetV3 model with Efficient Channel Attention (ECA). The experimental results indicate that the model’s accuracy on the test set is 99.8%, with a loss function value of 0.019. Building upon these results, this paper presents an experimental platform for real-time pork freshness detection, implemented by deploying the model on an intelligent gateway. The platform demonstrates stable performance with peak model memory usage under 600 MB, average CPU utilization below 20%, and gateway internal response times not exceeding 100 ms. Full article

A sandwich pallet loading problem represents a significant challenge in the logistics of fast-moving consumer goods (FMCG), requiring optimisation of load units (LUs) arrangements to minimise their number in transportation and warehousing processes, leading to an environmental responsibility of organisations. This study introduces an innovative approach combining Dominance-Based Rough Set Theory (DRST) with a rule-based expert system to improve the efficiency of the pallet loading and to provide sustainable development. Key criteria and attributes for the LU assessment, such as weight, height, and fragility, are defined. DRST is utilised to classify these units, leveraging its capability to handle imprecise and vague information. The rule-based system ensures an optimal arrangement of LUs by considering critical control parameters, thereby reducing LU numbers and mitigating the environmental impact of logistics operations, as measured by energy consumption. The proposed approach is validated using real-world data from the FMCG distribution company. Results demonstrate that integrating DRST with an expert system improves decision-making consistency and significantly reduces the number of LUs. This study shows a way to increase the level of environmental responsibility of the organisation by cutting energy consumption and delivering economic and social benefits through fewer shipments. For example, the approach reduces energy consumption for a customer order delivery by 40%, from 0.60 to 0.36 (kWh/pskm). Full article

Additive manufacturing and digital warehouses are transforming the way industries manage and maintain their spare parts inventory. Considering digital warehouses and on-demand manufacturing for spare parts during the project phase is a strategic decision that involves trade-offs depending on the operational needs and pricing structure. This paper aims to explore the spare part evaluation process considering both physical and digital warehouse inventories. A case asset is purposefully selected and four spare part management concepts are studied using a simulation modeling approach. The results highlight that the relevant digital warehouse scenario, used in this case, managed to completely reduce all emissions related to global spare parts supply; however, this was at the expense of reducing availability by 15.1%. However, the hybrid warehouse scenario managed to increase availability by 11.5% while completely reducing all emissions related to global spare parts supply. Depending on the demand rate, the digital warehousing may not be sufficient alone to keep the production availability at the highest levels; however, it is effective in reducing the stock amount, simplifying the inventory management, and making the supply process more green and resilient. A generic estimation model for spare parts engineers is provided to determine the optimal specifications of their spare parts supply and inventory while considering digital warehouses and on-demand manufacturing. Full article

This study investigates the transformation of e-commerce warehouse operations by integrating Lean Six Sigma tools to enhance efficiency and sustainability. Beginning with Value Stream Mapping (VSM) to identify inefficiencies, followed by a Hoshin Kanri plan to align improvement initiatives with strategic objectives, the study implemented measures such as pallet pooling, process standardization, automation in inspection and picking, layout optimization, and Kanban systems for continuous improvement. A case study of a local e-commerce warehouse specializing in medical devices and healthcare products identified 29 activities across receiving, inspection, storing, picking, packing, and shipping, highlighting inefficiencies addressed through Lean-driven initiatives. These efforts resulted in a 23% reduction in total lead time, doubled value-added time, and significant improvements in inspection, picking, packing, and automation, reducing delays, lowering costs, and enhancing workflow. The study fills a gap in the literature by integrating multiple Lean tools and utilizing the Critical to Quality (CTQ) matrix to ensure sustainable improvements in e-commerce warehousing, emphasizing the strategic value of Lean Six Sigma in creating efficient, customer-focused operations. Full article

Accelerating the construction and optimization of national value chains is of great significance to reducing both pollution and carbon emissions and promoting green economic growth. In accordance with the input–output table and carbon emission statistics of China in 2012, 2015, and 2017, in this paper, we use the total trade decomposition method and the value chain decomposition method to decompose the embodied carbon emissions and the embeddedness of national value chains. Subsequently, we empirically study, for the first time, the impact of the degree of domestic value chain embedding on implicit carbon emissions using the calculated results. The results show the following: (1) The top three provinces with embodied carbon emissions are Shandong, Hebei, and Jiangsu, while the top four industries are the production and supply of electricity and heat; metal smelting and rolling processing; non-metallic mineral products; and transportation, warehousing, and postal services. (2) The degree of forward and backward national value chain embeddedness in Chinese provinces has increased, and the degree of forward embeddedness in most provinces and industries is lower than that of backward embeddedness. (3) The embeddedness of domestic value chains and embodied carbon emissions is always negatively correlated, and this conclusion is still valid after robustness and endogeneity tests. (4) There is industrial heterogeneity in the impact of the degree of embeddedness of domestic value chains on embodied carbon emissions. Full article