Search Results (309)

Background: Integrating Radio Frequency Identification (RFID) technology into storage areas within the wiring harness manufacturing industry enables real-time component traceability and supports the implementation of fully automated inventory processes. While RFID systems provide continuous data regarding component type, quantity, and location, periodic inventory validation is still required to verify and correct records in the warehouse management system. Methods: This study examines the feasibility of passive ultra-high-frequency (UHF) RFID technology for automatic inventory management in a components warehouse. It also reviews relevant scientific literature on autonomous RFID signal measurement and Synthetic Aperture Radar (SAR)-based localization methods, which are subsequently adapted for inventory applications. An experimental setup is developed to characterize the reading field, hysteresis effects, and the influence of distance and tag orientation on detection performance. Results: The findings indicate that RFID-based automatic inventory is achievable with high accuracy and stability, especially when tag trajectories correspond to areas of high detection probability and antenna polarization is optimally configured. Conclusions: The proposed RFID-based system can be implemented with minimal hardware changes and low investment, thereby improving stock accuracy, traceability, and operational efficiency in automotive component logistics. Full article

AI technologies are increasingly applied to optimize operations in both port and urban logistics systems, yet integration across the full maritime city chain remains limited. The objective of this study is to assess, using a simulation-based experiment, the impact of an AI-orchestrated control policy on the performance of port–city logistics relative to a baseline scheduler. The study proposes an AI-orchestrated approach that connects autonomous ships, smart ports, central warehouses, and multimodal urban networks via a shared cloud control layer. This approach is designed to enable real-time, cross-domain coordination using federated sensing and adaptive control policies. To evaluate its impact, a simulation-based experiment was conducted comparing a traditional scheduler with an AI-orchestrated policy across 20 paired runs under identical conditions. The orchestrator dynamically coordinated container dispatching, vehicle assignment, and gate operations based on capacity-aware logic. Results show that the AI policy substantially reduced the total completion time, lowered truck idle time and estimated emissions, and improved system throughput and predictability without modifying physical resources. These findings support the expectation that integrated, data-driven decision-making can significantly enhance logistics performance and sustainability in port–city contexts. The study provides a replicable pathway from conceptual architecture to quantifiable evidence and lays the groundwork for future extensions involving learning controllers, richer environmental modeling, and real-world deployment in digitally connected logistics corridors. Full article

Origin warehouses, positioned at the critical “first mile” of the agri-food supply chain, profoundly influence supply chain power structures and profit allocation, as well as supply chain stability and sustainable development. To explore the role of origin warehouses in the agri-food supply chain, this study develops a three-level game model comprising a “planter–origin warehouse operator–seller” framework. Notably, this study conceptualizes the dual-functional “origin warehouse” as observed in practice, proposing two theoretical modes: the Logistics Center (LC) and the Supply Chain Center (SCC). By treating quality level, service level, and selling price decisions as endogenous variables, this study further reveals the interconnected decision-making mechanisms under different operational modes. Overall, the LC mode performs better in quality-driven markets, generating higher system profits and greater social welfare, whereas the SCC mode is superior when consumers are more price-sensitive or place greater value on service. Based on these findings, this study provides decision-making guidance for origin warehouse operators aiming to select the optimal mode under varying market conditions and proposes targeted coordination strategies to promote the high-quality development and economic sustainability of the agri-food supply chain. Full article

Mixed palletizing remains a core challenge in distribution centers and modern warehouse operations, particularly within robotic handling and automation systems. Progress in this domain has been hindered by the lack of realistic, freely available datasets for rigorous algorithmic benchmarking. This work addresses this gap by introducing MixedPalletBoxes, a family of seven synthetic datasets designed to evaluate algorithm scalability, adaptability and performance variability across a broad spectrum of workload sizes (500–100,000 records) generated via an open source Python script. These datasets enable the assessment of algorithmic behavior under varying operational complexities and scales. Each box instance is richly annotated with geometric dimensions, material properties, load capacities, environmental tolerances and handling flags. To support dynamic experimentation, the dataset is accompanied by a FastAPI-based tool that enables the on-demand creation of randomized daily picking lists simulating realistic inbound orders. Performance is analyzed through metrics such as pallet count, volume utilization, item distribution per pallet and runtime. Across all dataset sizes, the distributions of the physical attributes remain consistent, confirming stable generation behavior. The proposed framework combines standardization, feature richness and scalability, offering a transparent and extensible platform for benchmarking and advancing robotic mixed palletizing solutions. All datasets, generation code and evaluation scripts are publicly released to foster open collaboration and accelerate innovation in data-driven warehouse automation research. Full article

We presented a biomimetic approach to designing robot-to-human handover of objects in a collaborative assembly task. We developed a human–robot hybrid cell where a human and a robot collaborated with each other to perform the assembly operations of a product in a flexible manufacturing setup. Firstly, we investigated human psychology and biomechanics (kinetics and kinematics) for human-to-robot handover of an object in the human–robot collaborative set-up in three separate experimental conditions: (i) human possessed high trust in the robot, (ii) human possessed moderate trust in the robot, and (iii) human possessed low trust in the robot. The results showed that human psychology was significantly impacted by human trust in the robot, which also impacted the biomechanics of human-to-robot handover, i.e., human hand movement slowed down, the angle between human hand and robot arm increased (formed a braced handover configuration), and human grip forces increased if human trust in the robot decreased, and vice versa. Secondly, being inspired by those empirical results related to human psychology and biomechanics, we proposed a novel robot-to-human object handover mechanism (strategy). According to the novel handover mechanism, the robot varied its handover configurations and motions through kinematic redundancy with the aim of reducing potential impulse forces on the human body through the object during the handover when robot trust in the human was low. We implemented the proposed robot-to-human handover mechanism in the human–robot collaborative assembly task in the hybrid cell. The experimental evaluation results showed significant improvements in human–robot interaction (HRI) in terms of transparency, naturalness, engagement, cooperation, cognitive workload, and human trust in the robot, and in overall performance in terms of handover safety, handover success rate, and assembly efficiency. The results can help design and develop human–robot handover mechanisms for human–robot collaborative tasks in various applications such as industrial manufacturing and manipulation, medical surgery, warehouse, transport, logistics, construction, machine shops, goods delivery, etc. Full article

Over the past two decades, escalating climate crises, geopolitical conflicts, and pandemics have intensified the frequency and severity of disasters, exposing severe vulnerabilities in global food systems. In this pressing context, disaster nutrition emerges as a vital domain of intervention. However, existing academic literature and field practices often address this topic through fragmented, single-axis perspectives. Nutritional physiology, food technology, humanitarian logistics, and policy–ethics frameworks tend to progress in parallel yet disconnected tracks, which results in a lack of holistic models that adequately reflect field realities. The urgency of this issue is underscored by the latest global data. In 2023 alone, disasters resulted in over 86,000 deaths, a significant increase from the preceding two-decade annual average. Furthermore, the 2025 Global Report on Food Crises reveals that 295.3 million people faced high levels of acute food insecurity in 2024, marking the sixth consecutive year this number has risen. This escalating crisis highlights the inadequacy of fragmented approaches and necessitates the development of an integrated framework for disaster nutrition. To address this fragmentation, this study redefines disaster nutrition as a multi-layered, integrated food system challenge. Based on a comprehensive literature analysis, it proposes an “Integrated Disaster Food System Model” that brings these different dimensions together within a common framework. The model is built on four main components: (i) nutritional requirements and vulnerable groups (such as infants, older adults, pregnant individuals, and populations with chronic diseases requiring special diets); (ii) product design, technology, and packaging (balancing shelf life, nutritional value, cultural acceptability, and sensory attributes, including innovative components such as microalgae and fermented foods); (iii) logistics, storage, and distribution systems (centralized storage versus localized micro-warehouses, as well as the use of drones and digital traceability technologies); and (iv) policy, regulation, ethics, and sustainability (the applicability of the Sphere Standards, fair distribution, food waste, and environmental impact). By emphasizing the bidirectional and dynamic interactions among these components, the model demonstrates how decisions in one domain affect others (for example, how more durable packaging can increase both logistics costs and carbon footprint). The study highlights the risks and cultural mismatches associated with a “one-size-fits-all high-energy food” approach for vulnerable groups and argues for the necessity of localized, context-specific, and sustainable solutions. In conclusion, the article posits that the future of disaster food systems can only be shaped through a holistic approach in which interdisciplinary collaboration, technological innovation, and ethical–environmental principles are integrated into the core of policy-making. Full article

In industrial environments such as ports and warehouses, autonomous logistics vehicles face significant challenges in coordinating multiple vehicles while ensuring safe and efficient path planning. This study proposes a novel real-time cooperative control framework for autonomous vehicles, combining reinforcement learning (RL) and distributed model predictive control (DMPC). The RL agent dynamically adjusts the optimization weights of the DMPC to adapt to the vehicle’s real-time environment, while the DMPC enables decentralized path planning and collision avoidance. The system leverages multi-source sensor fusion, including GNSS, UWB, IMU, LiDAR, and stereo cameras, to provide accurate state estimations of vehicles. Simulation results demonstrate that the proposed RL-DMPC approach outperforms traditional centralized control strategies in terms of tracking accuracy, collision avoidance, and safety margins. Furthermore, the proposed method significantly improves control smoothness compared to rule-based strategies. This framework is particularly effective in dynamic and constrained industrial settings, offering a robust solution for multi-vehicle coordination with minimal communication delays. The study highlights the potential of combining RL with DMPC to achieve real-time, scalable, and adaptive solutions for autonomous logistics. Full article

Autonomous Unmanned Aerial Vehicles (UAVs) are widely used in smart agriculture, logistics, and warehouse management, where precise trajectory prediction is important for safety and efficiency. Traditional approaches require complex physical modeling including mass properties, moment of inertia measurements, and aerodynamic coefficient calculations, which creates significant barriers for custom-built UAVs. Existing trajectory prediction methods are primarily designed for motion forecasting from dense historical observations, which are unsuitable for scenarios lacking historical data (e.g., takeoff phases) or requiring trajectory generation from sparse waypoint specifications (4–6 constraint points). This distinction necessitates architectural designs optimized for spatial interpolation rather than temporal extrapolation. To address these limitations, we present a segmented LSTM framework for complete autonomous flight trajectory prediction. Given target waypoints, our architecture decomposes flight operations, predicts different maneuver types, and outputs the complete trajectory, demonstrating new possibilities for mission-level trajectory planning in autonomous UAV systems. The system consists of a global duration predictor (0.124 MB) and five segment-specific trajectory generators (∼1.17 MB each), with a total size of 5.98 MB and can be deployed in various edge devices. Validated on real Crazyflie 2.1 data, our framework demonstrates high accuracy and provides reliable arrival time predictions, with an Average Displacement Error ranging from 0.0252 m to 0.1136 m. This data-driven approach avoids complex parameter configuration requirements, supports lightweight deployment in edge computing environments, and provides an effective solution for multi-UAV coordination and mission planning applications. Full article

The widespread use of mezzanine racks in modern logistics warehouses has significantly increased fire hazards owing to the dense storage of combustibles. However, systematic full-scale studies examining the influence of shelf spacing on radiative ignition between adjacent racks are lacking. In this study, we investigate the effect of shelf spacing on radiative flame spread using full-scale fire tests and cone calorimeter experiments. The decrease in radiative heat flux with an increase in the distance was consistent with the inverse square law. Adjacent shelf ignition was prevented when the spacing was at least 5 m. Cone calorimeter tests identified a critical radiant heat flux of approximately 8 kW/m², and the ignition time decreased nonlinearly from 207.8 to 69.6 s as the radiant flux increased from 10 to 16 kW/m². These findings were cross-validated with the full-scale results, which indicated that a minimum spacing of 5 m serves as a radiative flame-spread barrier under similar storage and ventilation conditions. This study provides practical guidance for the fire-safety design of mezzanine rack warehouses. The effects of storage geometry, surface reflectivity, ventilation, active protection systems, and varying storage densities may be considered in future work to ensure broader applicability. Full article

This paper presents an original method for modelling the energy intensity of an overhead crane using MATLAB–Simulink and MSC Adams software. The analysis focused on an overhead crane used in warehouses handling bundled goods, which are placed on pallets. The study examined the energy intensity of the crane in two reference, predefined work cycles: goods reception and order picking. During the development phase, data from logistics centres and the FLEXSIM system were used to define the test cycles. The author’s experience in implementing and developing standards was also applied. Reference measurements of the crane, necessary for validating the computer model, were carried out in real operating conditions at a logistics centre. The integration of the author’s proprietary approach—combining computer-based energy intensity modelling with test cycles for the crane—helped overcome barriers in supporting the concept of “green warehouses” (passive or energy-positive), making it possible to estimate and compare the energy intensity of intralogistics facilities. A high level of agreement was achieved between the measured and modelled data using the author’s proprietary EPI. The described methodology was verified using a double-girder overhead crane handling bundled load units in a warehouse. The test results determined the potential for energy recovery within the crane’s drive system. Full article

Intelligent autonomous guided vehicles (AGVs) are of huge importance in facilitating the automation of load handling in the era of Industry 4.0. AGVs heavily rely on environmental perception, such as the 6D poses of objects, in order to execute complex tasks efficiently. Therefore, estimating the 6D poses of objects in warehouses is crucial for proper load handling in modern intra-logistics warehouse environments. This study presents a deep convolutional neural network approach for estimating the pose of warehouse load carriers. Recognizing the paucity of labeled real 6D pose estimation data, the proposed approach uses only synthetic RGB warehouse data to train the network. Domain adaption was applied using a Contrastive Unpaired Image-to-Image Translation (CUT) Network to generate domain-adapted training data that can bridge the domain gap between synthetic and real environments and help the model generalize better over realistic scenes. In order to increase the detection range, a multi-stage refinement detection pipeline is developed using consistent multi-view multi-object 6D pose estimation (CosyPose) networks. The proposed framework was tested with different training scenarios, and its performance was comprehensively analyzed and compared with a state-of-the-art non-adapted single-stage pose estimation approach, showing an improvement of up to 80% on the ADD-S AUC metric. Using a mix of adapted and non-adapted synthetic data along with splitting the state space into multiple refiners, the proposed approach achieved an ADD-S AUC performance greater than 0.81 over a wide detection range, from one and up to five meters, while still being trained on a relatively small synthetic dataset for a limited number of epochs. Full article

Addressing two types of supply chain disruptions—frequent short-duration disruptions (e.g., minor natural disasters) and infrequent long-duration disruptions (e.g., geopolitical conflicts, public health crises)—while considering their impact on logistics capacity, this paper proposes a multi-echelon inventory management optimization framework based on the Proximal Policy Optimization (PPO) algorithm. Unlike traditional inventory control models with simplistic assumptions, this study integrates factors such as the frequency, duration, and impact of disruptions into the inventory optimization process. It is designed to coordinate replenishment decisions at the warehouse while reacting to local retailer states. Since retailers share the same cost parameters and demand dynamics, their decision problems are structurally symmetric, which allows us to use a shared policy across retailers and thus keep the learning model compact and scalable. Numerical experiments compare the PPO policy with classical inventory heuristics under various network sizes and disruption types. The results show that PPO consistently achieves lower total costs than the benchmarks, and its relative advantage becomes more pronounced under severe or longer disruptions. These findings suggest that modern policy-gradient methods, combined with simple forms of structural symmetry, can provide an effective and scalable tool for managing disrupted multi-echelon supply chains. Full article

This systematic review focuses on intelligent navigation as a core enabler of autonomy in smart warehouses, where mobile robots must dynamically perceive, reason, and act in complex, human-shared environments. By synthesizing advancements in AI-driven decision-making, SLAM, and multi-sensor fusion, the study highlights how intelligent navigation architectures reduce operational uncertainty and enhance task efficiency in logistics automation. Smart warehouses, powered by mobile robots and AGVs and integrated with AI and algorithms, are enabling more efficient storage with less human labour. This systematic review followed PRISMA 2020 guidelines to systematically identify, screen, and synthesize evidence from 106 peer-reviewed scientific articles (including pri-mary studies, technical papers, and reviews) published between 2020–2025, sourced from Web of Science. Thematic synthesis was conducted across 8 domains: AI, SLAM, sensor fusion, safety, network, path planning, implementation, and design. The transition to smart warehouses requires modern technologies to automate tasks and optimize resources. This article examines how intelligent systems can be integrated with mathematical models to improve navigation accuracy, reduce costs and prioritize human safety. Real-time data management with precise information for AMRs and AGVs is crucial for low-risk operation. This article studies AI, the IoT, LiDAR, machine learning (ML), SLAM and other new technologies for the successful implementation of mobile robots in smart warehouses. Modern technologies such as reinforcement learning optimize the routes and tasks of mobile robots. Data and sensor fusion methods integrate information from various sources to provide a more precise understanding of the indoor environment and inventory. Semantic mapping enables mobile robots to navigate and interact with complex warehouse environments with high accuracy in real time. The article also analyses how virtual reality (VR) can improve the spatial orientation of mobile robots by developing sophisticated navigation solutions that reduce time and financial costs. Full article

Background: Throughout the course of the COVID-19 pandemic, clinicians recognized that individuals with metabolic syndrome, including elevated body mass index (BMI) and type 2 diabetes, have increased clinical care requirements and worsened outcomes during COVID-19 infection. With the availability of COVID-19 vaccines, it was unknown whether vaccination could mitigate the clinical outcomes among patients with metabolic syndrome. In this study, we sought to determine whether BMI and hemoglobin A1c are associated with a risk of breakthrough infection and increased clinical needs among patients who have been fully vaccinated against COVID-19. Methods: We conducted a retrospective cohort study of patients in the Veterans Affairs healthcare system who were vaccinated against COVID-19 between 1 December 2020 and 22 August 2021. We sampled a random subset of 549,344 patients from a total of over 1 million de-identified patients greater than age 18 who were vaccinated between 1 December 2020 and 22 August 2021, without a prior positive COVID-19 test in the VA healthcare system data warehouse. The primary study outcomes were breakthrough COVID-19 infections after vaccination and hospitalization due to breakthrough COVID-19 infections. Results: We identified 480,129 patients with available BMI and hemoglobin A1c data; of these, all had data available for the covariates of race, ethnicity, sex, and age, and 467,283 had data available for district as well. Adjusting for those covariates, Cox proportional hazards modeling for time from vaccination until breakthrough infection demonstrated that higher BMI (HR per unit 1.015, p < 0.001) and hemoglobin A1c were associated with an increased risk of infection (HR per unit 1.063, p < 0.001). The number of patients from this set who developed breakthrough infections within the study period was 8903 (9146 if those with missing district data were included). The average age of fully vaccinated patients with breakthrough COVID-19 infection within six months of full vaccination was 64.5. The average BMI was 31.2 ± 6.2 and the average A1c was 6.34 ± 1.5. Adjusting for the above covariates, multivariable logistic regression trends towards significance, with an increased risk of hospitalization due to breakthrough COVID-19 infection with increased BMI (HR per unit 1.010, p = 0.052), and was statistically significant for increased hemoglobin A1c (HR per unit 1.150, p < 0.010). Conclusions: This study identifies BMI and hemoglobin A1c as risk factors for breakthrough COVID-19 infection among fully vaccinated patients in the US veteran population. Full article

The technocratic and economically driven approach to urban and industrial planning can be observed in the evolution of modern logistics parks, which have become key infrastructural elements of regional development. This study explores how technocratic logic influences the spatial and environmental transformation of logistics parks in Poland within the context of sustainable certification systems such as BREEAM International (Building Research Establishment Environmental Assessment Method). The research employed an eight-stage methodological framework combining exploratory, analytical, and empirical methods. The process began with a comprehensive literature and data query on the development of logistics parks at global, European, and national levels, followed by a systematic review of sustainability assessment systems. A research framework was then defined to establish a consistent model of warehouse buildings, verified through the PLGBC database of certified facilities. The dataset was filtered and standardized, and a purposive sample of 25 BREEAM-certified warehouses was selected from 150 eligible cases. Each certification report was analyzed to identify credit distribution patterns, and the results were examined through a factor analysis to interpret the technocratic and systemic determinants influencing sustainability decisions. The findings reveal that the decision-making logic of developers is dominated by quantitative optimization and regulatory alignment, leading to the prioritization of low-cost, easily verifiable credits such as ENE03 (External Lighting) and WAT02 (Water Monitoring), while complex or innovative credits such as MAT06 (Material Efficiency) remain under implemented. The study contributes to the understanding of how technocratic rationality shapes sustainable certification outcomes and highlights the need for stronger policy and market incentives to promote circular and systemic approaches in the logistics real estate sector. Full article