Search Results (59)

In an era of exponential data growth, ensuring high data quality has become essential for effective, evidence-based decision making. This study presents a structured and comparative review of the field by integrating data classifications, quality dimensions, assessment methodologies, and modern software tools. Unlike earlier reviews that focus narrowly on individual aspects, this work synthesizes foundational concepts with formal frameworks, including the Findable, Accessible, Interoperable, and Reusable (FAIR) principles and the ISO/IEC 25000 series on software and data quality. It further examines well-established assessment models, such as Total Data Quality Management (TDQM), Data Warehouse Quality (DWQ), and High-Quality Data Management (HDQM), and critically evaluates commercial platforms in terms of functionality, AI integration, and adaptability. A key contribution lies in the development of conceptual mappings that link data quality dimensions with FAIR indicators and maturity levels, offering a practical reference model. The findings also identify gaps in current tools and approaches, particularly around cost-awareness, explainability, and process adaptability. By bridging theory and practice, the study contributes to the academic literature while offering actionable insights for building scalable, standards-aligned, and context-sensitive data quality management strategies. Full article

This article presents a model for orchestrating data extraction, processing, and storage, addressing the challenges posed by diverse data sources and increasing data volumes. The proposed model includes three primary components: data production, data transfer, and data consumption and storage. Key architectures for data production are explored, such as modular designs and distributed processes, each with advantages and limitations regarding scalability, fault tolerance, and resource efficiency. A buffering module is introduced to enable temporary data storage, ensuring resilience and asynchronous processing. The data consumption module focuses on transforming and storing data in data warehouses while providing options for parallel and unified processing architectures to enhance efficiency. Additionally, a notification module demonstrates real-time alerts based on specific data events, integrating seamlessly with messaging platforms like Telegram. The model is designed to ensure adaptability, scalability, and robustness for modern data-driven applications, making it a versatile solution for effective data flow management. Full article

Efficient material handling is crucial in the logistics operations of modern salt warehouses, where Rail Guided Vehicles (RGVs) and Air Sorting Robots (ASRs) are often deployed to manage inbound and outbound tasks. However, as the number of tasks increases within a given period, conflicts and deadlocks between simultaneously operating RGVs and ASRs become more frequent, reducing efficiency and increasing energy consumption during transportation. To address this, the research frames the inbound and outbound problem as a task allocation issue for the RGV/ASR system with a finite buffer, and proposes a collision avoidance strategy and a zero-wait strategy for loaded machines to reallocate tasks. To improve computational efficiency, we introduce an adaptive multi-neighborhood hybrid search (AMHS) algorithm, which integrates a dual-sequence coding scheme and an elite solution initialization strategy. A dedicated global search operator is designed to expand the search landscape, while an adaptive local search operator, inspired by biological hormone regulation mechanisms, along with a perturbation strategy, is used to refine the local search. In a case study on packaged salt storage, the proposed AMHS algorithm reduced the total makespan by 30.1% compared to the original task queue. Additionally, in 15 randomized test scenarios, AMHS demonstrated superior performance over three benchmark algorithms—Genetic Algorithm (GA), Discrete Imperialist Competitive Algorithm (DICA), and Improved Whale Optimization Algorithm (IWOA)—achieving an average makespan reduction of 12.6% relative to GA. Full article

Modern warehouses play a fundamental role in today’s logistics, serving as strategic hubs for the reception, storage, and distribution of goods. However, training warehouse operators presents a significant challenge due to the complexity of logistics processes and the need for efficient and engaging learning methods. Training in logistics operations requires practical experience and the ability to adapt to real-world scenarios, which can result in high training costs. In this context, gamification and artificial intelligence emerge as innovative solutions to enhance training by increasing operator motivation, reducing learning time, and optimizing costs through personalized approaches. But is it possible to effectively apply these techniques to logistics training? This study introduces WarehouseGame Training, a gamified training tool developed in collaboration with Mecalux Software Solutions and implemented in Unity 3D. The solution integrates large language models (LLMs) such as ChatGPT, DeepSeek, and Grok to enhance adaptive learning. These models dynamically adjust challenge difficulty, provide contextual assistance, and evaluate user performance in logistics training scenarios. Through this gamified training tool, the performance of these AI models is analyzed and compared, assessing their ability to improve the learning experience and determine which one best adapts to this type of training. Full article

Automated storage and retrieval systems (AS/RS) play a crucial role in modern logistics, yet effectively monitoring cargo stacking patterns remains challenging. While computer vision and deep learning offer promising solutions, existing methods struggle to balance detection accuracy, computational efficiency, and environmental adaptability. This paper proposes a novel machine vision-based detection algorithm that integrates a pallet surface object detection network (STEGNet) with a box edge detection algorithm. STEGNet’s core innovation is the Efficient Gated Pyramid Feature Network (EG-FPN), which integrates a Gated Feature Fusion module and a Lightweight Attention Mechanism to optimize feature extraction and fusion. In addition, we introduce a geometric constraint method for box edge detection and employ a Perspective-n-Point (PnP)-based 2D-to-3D transformation approach for precise pose estimation. Experimental results show that STEGNet achieves 93.49% mAP on our proposed GY Warehouse Box View 4-Dimension (GY-WSBW-4D) dataset and 83.2% mAP on the WSGID-B dataset, surpassing existing benchmarks. The lightweight variant maintains competitive accuracy while reducing the model size by 34% and increasing the inference speed by 68%. In practical applications, the system achieves pose estimation with a Mean Absolute Error within 4 cm and a Rotation Angle Error below 2°, demonstrating robust performance in complex warehouse environments. This research provides a reliable solution for automated cargo stack monitoring in modern logistics systems. Full article

Rapid changes in the modern world and technological advances and processes are increasingly contributing to greater attention being given to emerging problems associated with obtaining big data, as well as modifying decision-making processes in diverse spheres. Special attention in logistics companies should be given to the warehouse as a critical component of logistics, in particular to such processes as big data processing and automation, as well as the improvement, development, and support of information systems. Enhancing logistics information systems provides companies with a competitive advantage, reduces the emergence of human error, accelerates processes, and ensures the collection and sharing of information and big data are used in a sustainable manner. The automation of warehouse processes results in better-established operational safety and overall service quality. The present paper reviews the importance of improving warehouse automation and logistics information systems. Its advantages are highlighted, and the results of the conducted research are provided to expose the problem areas of warehouse automation and encourage improvements in information systems in Lithuanian logistics companies wherein there is a need to transfer a large amount of information and increase service quality. Full article

As product diversity continues to expand in today’s market, there is an increasing demand from customers for unique and varied items. Meeting these demands necessitates the transfer of different sub-product components to the production line, even within the same manufacturing process. Lean manufacturing has addressed these challenges through the development of kitting systems that streamline the handling of diverse components. However, to ensure that these systems contribute to sustainable practices, it is crucial to design and implement them with environmental considerations in mind. The optimization of warehouse layouts and kitting preparation areas is essential for achieving sustainable and efficient logistics. To this end, we propose a comprehensive study aimed at developing the optimal layout, that is, creating warehouse layouts and kitting preparation zones that minimize waste, reduce energy consumption, and improve the flow of materials. The problem of warehouse location assignment is classified as NP-hard, and the complexity increases significantly when both storage and kitting layouts are considered simultaneously. This study aims to address this challenge by employing the genetic algorithm (GA) and Ant Colony Optimization (ACO) methods to design a system that minimizes energy consumption. Through the implementation of genetic algorithms (GAs), a 24% improvement was observed. This enhancement was achieved by simultaneously optimizing both the warehouse layout and the kitting area, demonstrating the effectiveness of integrated operational strategies. This substantial reduction not only contributes to lower operational costs but also aligns with sustainability goals, highlighting the importance of efficient material handling practices in modern logistics operations. This article provides a significant contribution to the field of sustainable logistics by addressing the vital role of kitting systems within green supply chain management practices. By aligning logistics operations with sustainability goals, this study not only offers practical insights but also advances the broader conversation around environmentally conscious supply chain practices. Full article

In the municipality of La Granja d’Escarp, for over thirty years, an important natural cement factory was in operation. In 1876, the Girona family, who were businessmen and bankers from Barcelona, opened the factory with modern industrial facilities. It included kilns, mills, and crushers, alongside warehouses, a small railway for internal transportation of the various materials used, and even a housing area for workers. The neighboring Ebro River allowed distribution by river transport at first. Later, with the use of railways, transport to consumption points was possible. This industrial complex became a center of significant importance in Catalonia in the production of cement, which was used for building hydraulic and civil works. During the first decade of the twentieth century, the factory stopped its activity and the facilities were abandoned. Nowadays, this industrial heritage site is in a state of neglect, without any kind of protection or maintenance. In turn, this has caused the collapse of some buildings in recent times and the loss of historical value of the architectural ensemble. We have carried out initial geomatic research, which has highlighted the constructive properties of the kilns. We have also tested five samples from different buildings using XRD and TGA/DSC, which showed the use of lime mortars in their construction. This is the first study to be carried out at this site, with the aim of showing the historical importance of the ensemble. The goal of the study was to highlight the value of this industrial heritage site and illustrate that it was once a pioneer in the production of natural cement and a driving force for Catalonia. Full article

Industrial architecture is the result of the integration of complex planning and construction, with the goal of attaining an optimal arrangement of building processes toward the creation of a quality working environment. The subject of research focuses on the architecture of light-industry buildings and product warehouses and includes sustainable smart concepts and laboratories for modern industry with high-quality production and working environments. All of this is expressed in the creation of architecture as a result of the meaningful dialogue among the components of architectural design. The goal of this research is to determine the main categories of the interaction of industrial architecture and construction and, at the same time, to provide an answer to the main research question of what the application determinates are in a given relationship: environment—architectural expression—construction. The quantitative and qualitative methods of research are focused on the choice, definition, and correlation (dialogue) of the elements of architecture and construction, in dependence on the character of the industrial activity. The research outputs, in the form of diagrams and illustrative graphic displays, make a contribution toward the visual interpretation of the architecture/construction relationship and the methodological basis for the creative process of designing industrial architecture within the context of contemporary trends. Their use in engineering and architecture education is of undoubted significance. Full article

The goal of this study was to determine how the loss of dates affected food security in Saudi Arabia from 2000 to 2021. The researchers achieved this by using food security indicators, economic equations to quantify agricultural resource losses, and econometric analyses to develop a partial adjustment model. The results show that dates are considered a self-sufficient crop as well as an export crop, as the state resorts to exporting the surplus instead of storing it for local consumption. During the study period, there was an increase in the period of sufficiency in date production for local consumption and the period of coverage of imports for local consumption. In 2000, the volume of dates lost increased by approximately 131.22%. The total loss of land and water resources reached 31,918.4 hectares and 324.759 million cubic meters, respectively. The value of the partial adjustment coefficient (λ) indicates that about 81.1% of the imbalance in the equilibrium between the actual and equilibrium levels is adjusted within one year. Loss is considered one of the most important factors that determine food security for dates, in addition to the total value of loans financed to date factories, the total population, and the level of technological progress in the marketing and storage of dates. It was found that a change of 10% in these variables leads to a change in food security for dates of (−2.37%), 0.07, (−15.33%), and 0.58%, respectively, and the adjusted coefficient of determination was estimated at 0.93. This study recommends the following: (1) expanding the use of modern technologies for date post-harvest transportation and storage, and (2) increasing support and loans allocated to date factories to increase warehouses for cooling and storage to accommodate the increase in production and surplus consumption. Full article

Data democratization (DD) is a new concept rapidly becoming a game-changer, enabling companies to innovate and maintain a competitive edge in a data-driven world. This paper explores the evolution of data accessibility, from the early days of manual record-keeping to the sophisticated data management systems of today. The evolution from transactional databases to data warehouses marked a shift toward centralized data management and specialized teams, supporting the standard principles of DD contexts such as data governance (DG), privacy, management, usability, accessibility, and literacy. This paper provides an overview of the evolution of data access, from manual record-keeping to the modern data management systems of today, focusing on the challenges related to data privacy and security, integration of legacy systems, and the cultural shift required to embrace a data-driven mindset. This paper also explores both universal and holistic approaches to DD, assessing the challenges, benefits, and possibilities of their applications. An overview of industry-specific cases is included in the paper to provide practical insights that would contribute to understanding the most effective approach to data democratization. Full article

This study explores enhancing decision-making processes in inventory management and production operations by integrating a developed system. The proposed solution improves the decision-making process, managing the material supply of the product and inventory management in general. Based on the researched issues, the shortcomings of modern enterprise resource planning systems (ERPs) were considered in the context of Warehouse 4.0. Based on the problematic areas of material accounting in manufacturing enterprises, a typical workplace was taken as a basis, which creates a gray area for warehouse systems and does not provide the opportunity of quality-managing the company’s inventory. The main tool for collecting and processing data from the workplace was the neural network. A mobile application was proposed for processing and converting the collected data for the decision-maker on material management. The YOLOv8 convolutional neural network was used to identify materials and production parts. A laboratory experiment was conducted using 3D-printed models of commercially available products at the SmartTechLab laboratory of the Technical University of Košice to evaluate the system’s effectiveness. The data from the network evaluation was obtained with the help of the ONNX format of the network for further use in conjunction with the C++ OpenCV library. The results were normalized and illustrated by diagrams. The designed system works on the principle of client–server communication; it can be easily integrated into the enterprise resource planning system. The proposed system has potential for further development, such as the expansion of the product database, facilitating efficient interaction with production systems in accordance with the circular economy, Warehouse 4.0, and lean manufacturing principles. Full article

Though refugee camps are by definition “temporary”, many camps endure for decades, where individuals live full lives through childhood, marriage, children, grandchildren, and death. These settlements function no differently than cities in their social life, density, zoning, and operation, yet are “planned” through UNHCR (United Nations High Commissioner for Refugees) templates for camps. The Zaatari camp in Jordan for Syrian asylum seekers, for example, holds a population of 80,000. Rather than viewing refugee camps as temporary human warehouses, this article demonstrates that camps are spaces where individuals build social networks and economic activities flourish. As such, the camp planning templates should include adaptive Construction 4.0 technologies for more socially flexible settlements, even if the camps are considered “temporary”. This case study research on the Zaatari camp illustrates how refugees adapt their built environment, identifying adaptation patterns that enhance both livability and sustainability. The work illustrates social and environmental changes that require adaptive housing configurations. The conclusion suggests linking modern tools in the construction industry to empirically derived planning objectives to be efficiently executed in moments of crisis. Full article

As a product of modern development, logistics plays a significant role in economic growth with its advantages of integrated management, unified operations, and speed. With the rapid advancement of technology and economy, traditional manual storage and retrieval methods can no longer meet industry demands. Achieving efficient storage and retrieval of goods on densely packed, symmetrically shaped logistics shelves has become a critical issue that needs urgent resolution. The brain storm optimization (BSO) algorithm, introduced in 2010, has found extensive applications across various fields. This paper presents a modified BSO algorithm (MBSO) aimed at addressing the scheduling challenges of double-end automated storage and retrieval systems (DE-AS/RSs). Traditional AS/RSs suffer from slow scheduling efficiency and the current heuristic algorithms exhibit low accuracy. To overcome these limitations, we propose a new scheduling strategy for the stacker to select I/O stations in DE-AS/RSs. The MBSO incorporates two key enhancements to the basic BSO algorithm. First, it employs an objective space clustering method in place of the standard k-means clustering to achieve more accurate solutions for AS/RS scheduling problems. Second, it utilizes a mutation operation based on a greedy strategy and an improved crossover operation for updating individuals. Extensive comparisons were made between the well-known heuristic algorithms NIGA and BSO in several specific enterprise warehouse scenarios. The experimental results show that the MBSO has significant accuracy, optimization speed, and robustness in solving scheduling of AS/RSs. Full article

A Data Warehouse (DW) is a centralized database that stores large volumes of historical data for analysis and reporting. In a world where enterprise data grows exponentially, new architectures are being investigated to overcome the deficiencies of traditional Database Management Systems (DBMSs), driving a shift towards more modern, cloud-based solutions that provide resources such as distributed processing, columnar storage, and horizontal scalability without the overhead of physical hardware management, i.e., a Database as a Service (DBaaS). Choosing the appropriate class of DBMS is a critical decision for organizations, and there are important differences that impact data volume and query performance (e.g., architecture, data models, and storage) to support analytics in a distributed cloud environment efficiently. In this sense, we carry out an experimental evaluation to analyze the performance of several DBaaS and the impact of data modeling, specifically the usage of a partially normalized Star Schema and a fully denormalized Flat Table Schema, to further comprehend their behavior in different configurations and designs in terms of data schema, storage form, memory availability, and cluster size. The analysis is done in two volumes of data generated by a well-established benchmark, comparing the performance of the DW in terms of average execution time, memory usage, data volume, and loading time. Our results provide guidelines for efficient DW design, showing, for example, that the denormalization of the schema does not guarantee improved performance, as solutions performed differently depending on its architecture. We also show that a Hybrid Processing (HTAP) NewSQL solution can outperform solutions that support only Online Analytical Processing (OLAP) in terms of overall execution time, but that the performance of each query is deeply influenced by its selectivity and by the number of join functions. Full article