Search Results (27)

The efficiency of emergency response operations is critically dependent on the strategic storage and allocation of emergency supplies. Proper management of these resources reduces economic impacts and ensures prompt availability in crises. This study addresses the challenges and existing practices in emergency reserve warehousing, with a specific focus on a Fangshan District case study. It introduces optimized storage strategies and principles for storage location assignment, taking into account both planar and three-dimensional storage configurations. The study employs two pallet types to establish basic assumptions and formulates two models: one for standard pallets in three-dimensional storage and another for special pallets in planar storage, including scenarios for their combined usage. Utilizing an advanced non-dominated genetic algorithm (NSGA-II) with an elite strategy, the study conducts simulations and analyses of these models under various scenarios. The findings indicate that the application of the second scenario significantly improves storage location optimization in emergency reserve warehouses. Full article

As global trade continues to intensify, the role of pallets becomes increasingly crucial, as they are essential for the movement of goods worldwide. Wooden pallets are the most common packaging type in Italy and Europe, and their widespread use in distribution and freight transportation means that the relatively minor environmental impact of an individual pallet is greatly magnified by the overall scale of operations. The management of pallets can significantly influence both the emissions and the costs associated with pallet operations. This work presents a case study representative of the emerging trends in sustainable transportation and logistics in Italy, aiming to compare the carbon footprint of the standard pallet exchange system with the system employed by the company Nolpal. Unlike the conventional exchange model, which requires companies to purchase and own EPAL pallets, Nolpal provides leased pallets to the market across Italy, supported by a nationwide network of collection hubs. A comparative life cycle assessment (LCA) between the Nolpal system and the conventional pallet exchange system showed that Nolpal’s approach achieves a 35% reduction in CO_2-eq emissions. These findings highlight how the company’s model could serve as a blueprint for future advancements in more sustainable pallet management strategies. Full article

Among the most concerning threats impacting global forest ecosystems is the pinewood nematode (Bursaphelenchus xylophilus (Steiner and Buhrer, 1934) Nickle, 1970), the causal agent of pine wilt disease. In Europe, effective management of this pest requires comprehensive regulatory and monitoring strategies, including the annual collection of thousands of wood samples from symptomatic trees and their surroundings, inspection of wood packaging materials like pallets, and the trapping of the insect vector, Monochamus spp., through national networks. Insects and wood samples are sent to official laboratories, where the latter are sometimes incubated at 25 °C for 15 days, aiming to maximize the probability of the detection of the nematode. This study expected to elucidate the effect of the wood incubation process on the detection of B. xylophilus by analyzing wood samples from pallets and green wood obtained from pine stands, both harbouring nematodes in adult and juvenile stages. Additionally, the investigation sought to assess how the presence of fungi, which serve as a food source for the nematodes, enables B. xylophilus to persist in treated pallet wood that is colonized by these fungi. The results indicated that the incubation period is unnecessary for detecting B. xylophilus in pallets, except when the wood is heavily colonized by fungi providing suitable nutrition for the nematodes, although such occurrences are expected to be rare. Furthermore, this study found no significant differences in population growth between the two stages of the nematode’s life cycle. This suggests that second-stage juveniles present in wood samples, despite not undergoing sexual differentiation, do not hinder the reproductive capacity of B. xylophilus. The risk of a potential infestation in treated pallet wood is unlikely if the treatment has been performed correctly, and the incubation does not contribute to increasing the probability of detecting the PWN. Conversely, for samples obtained from trees, the incubation period significantly enhances nematode detection. Full article

As the use of passive ultra-high frequency (UHF) radio frequency identification (RFID) tags continues to surge in supply chain management, it becomes crucial to optimize their application at various levels of packaging to ensure reliability. These packaging levels play a pivotal role in achieving maximum readability and widespread adoption within the industry. This research paper aims to determine the most suitable passive UHF RFID tag for consumer goods filled with liquid and wrapped in foil packaging. In this study, two distinct RFID tags from separate manufacturers were evaluated. The research focused on critical factors such as reader height, distance, and item configuration across different packaging levels (item, case, and pallet). The results demonstrated that the packaging configuration impacts the readability of RFID tags at each packaging level. Through rigorous testing, it was found that achieving a tag readability rate higher than 99.7% is feasible and readability can be optimized by adjusting the reader position, packaging configuration, and tag design. The optimized configuration and testing platform developed in this study can be used for comparable products in other supply chains such as consumer goods, pharmaceuticals, and food. The results of this study emphasize RFID’s potential to revolutionize supply chain management. Full article

Background: This study explores forklift operators’ situational awareness (SA) and human errors in logistic operations using a multiphase approach as an innovative methodology. Methods: Ethnography, eye tracking, error taxonomy, and retrospective think-aloud (RTA) were used to study the diverse cognitive, behavioral, and operational aspects affecting SA. After analyzing 566 events across 18 tasks, this research highlighted eye tracking’s potential by offering real-time insights into operator behavior and RTA’s potential as a method for cross-checking the causal factors underlying errors. Results: Critical tasks, like positioning forklifts and lowering pallets, significantly impact incident occurrence, while high-cognitive demand tasks, such as hoisting and identifying pedestrians/obstacles, reduce SA and increase errors. Driving tasks are particularly vulnerable to errors and are the most affected by operator risk generators (ORGs), representing 42% of incident risk events. This study identifies driving, hoisting, and lowering loads as the tasks most influenced by system factors. Limitations include the task difficulty levels, managing physical risk, and training. Future research is suggested in autonomous industrial vehicles and advanced driver assistance systems (ADASs). Conclusions: This study provides valuable insights into how we may improve safety in logistics operations by proposing a multiphase methodology to uncover the patterns of attention, perception, and cognitive errors and their impact on decision-making. Full article

Background: This review addresses the emerging field of automated packing cells, which lies at the intersection of robotics and packing problems. Integrating these two fields is critical for optimizing logistics and e-commerce operations. The current literature focuses on packing problems or specific robotic applications without addressing their integration. Methods: To bridge this gap, we conducted a comprehensive review of 46 relevant studies, analyzing various dimensions, including the components of robotic packing cells, the types of packing problems, the solution approaches, and performance comparisons. Results: Our review reveals a significant trend towards addressing online packing problems, which reflects the dynamic nature of logistics operations where item information is often incomplete. We also identify several research gaps, such as the need for standardized terminologies, comprehensive methodologies, and the consideration of real-world constraints in robotic algorithms. Conclusions: This review uniquely integrates insights from robotics and packing problems, providing a structured framework for future research. It highlights the importance of considering practical robotic constraints. It proposes a research structure that enhances the reproducibility and comparability of results in real-world scenarios. By doing so, we aim to guide future research efforts and facilitate the development of more robust and practical automated packing systems. Full article

Logistics and the supply chain are areas of great importance within organizations. Due to planning gaps, an increase in extra and unnecessary transport costs is usually observed in several companies due to their commercial commitments and need to comply with the delivery time and the batch quantity of products, leading to a negative economic impact. Thus, the objective of this work was to adjust an optimization model to maximize the shipments usually carried out by the companies. To validate the model, an automotive components manufacturer was selected, allowing us to apply the model to a real case study and evaluate the advantages and drawbacks of this tool. It was found that the company to validate the model exports most of its products, and most pallets sent are not fully optimized, generating excessive expense for the company in terms of urgent transport. To solve this problem, two mathematical optimization models were used for the company’s current reality, optimizing the placement of boxes per pallet and customer. With the use of the new tool, it was possible to determine that five pallets should be sent to the customer weekly, which correspond to their needs, and that have the appropriate configurations so that the pallet is sent completely. Full article

In the context of increasingly competitive shipbuilding, the flexible multi-level picking system, composed of high-rise shelves, Automated Guided Vehicles (AGVs), and picking stations, has been of gradual interest because of its advantages in operation efficiency, system flexibility, and system robustness. Compared with other simple-level systems, the flexible multi-level picking system has a more complex coupling temporal relationship, which makes the scheduling optimization of shipbuilding automated collaborative order picking (SACOP) extremely difficult. In order to avoid the dilemma of finding a feasible and optimal collaborative scheduling scheme under the constraints of a complex temporal relationship, this paper proposed a multi-AGV-driven pallet-picking scheduling optimization (MADPSO) method, which takes the AGV scheduling scheme as the direct solution and modifies it to a feasible solution under the reasonably designed interaction strategy of stacker, AGV, and the interaction strategy of picking station, AGV. Furthermore, taking the minimum energy consumption and operation time as the optimization objectives, a multi-objective optimization mathematical model was established to describe MADPSO, and an improved NSGA-III algorithm was designed to solve the problem. Finally, several experiments were conducted in various scenarios and verified that using MADPSO can achieve a comprehensive optimization index improvement of 52.02–75.66% compared with traditional picking methods, which has a certain reference significance for shipyards. Full article

In the context of post-harvest pest management in agricultural products, the adoption of modified atmospheres presents an eco-friendly alternative to conventional pesticides. This study focuses on nitrogen gas as a potential agent for insect control in stored commodities, utilizing computational simulations (by employing the convection–diffusion equation) to investigate its penetration and distribution within two common storage configurations: chamber-contained pallets and silos. The results highlight the influence of boundary conditions, commodity porosity, and convection effects on nitrogen dispersion. In chamber scenarios, the first boundary condition considers that pallets are placed inside a chamber with uniform (99.5%) nitrogen concentration, whereas in the second one, the concentration gradually increases from 78% to 99.5%. The average duration required for O${}_2$ concentration to reach 1% is approximately 10.7 h and 133.3 h for the two boundary conditions, respectively. Among the agricultural commodities, walnuts (kernels) exhibit the shortest duration, while prunes require the longest time. In silos, convection and diffusion interact to establish a consistent diffusion layer thickness. Most agricultural products exhibit similar behavior, with average times of 13.5 h, 25.4 h, and 37.0 h for three heights (10 m, 20 m, and at the silo’s top at 30 m), respectively. Full article

A typical retailer carries 10,000 stock-keeping units (SKUs). However, these numbers may exceed hundreds of millions for giants such as Walmart and Amazon. Besides the volume, SKU data can also be high-dimensional, which means that SKUs can be segmented on the basis of various attributes. Given the data volumes and the multitude of potentially important dimensions to consider, it becomes computationally impossible to individually manage each SKU. Even though the application of clustering for SKU segmentation is common, previous studies do not address the problem of parametrization and model finetuning, which may be extremely tedious and time-consuming in real-world applications. Our work closes the research gap by proposing a solution that leverages automated machine learning for the automated cluster analysis of SKUs. The proposed framework for automated SKU segmentation incorporates minibatch K-means clustering, principal component analysis, and grid search for parameter tuning. It operates on top of the Apache Parquet file format, an efficient, structured, compressed, column-oriented, and big-data-friendly format. The proposed solution was tested on the basis of a real-world dataset that contained data at the pallet level. Full article

Digitization and virtualization represent key factors in the era of Industry 4.0. Digital twins (DT) can certainly contribute to increasing the efficiency of various productive sectors as they can contribute to monitoring, managing, and improvement of a product or process throughout its life cycle. Although several works deal with DTs, there are gaps regarding the use of this technology when a Flexible Manufacturing System (FMS) is used. Existing work, for the most part, is concerned with simulating the progress of manufacturing without providing key production data in real-time. Still, most of the solutions presented in the literature are relatively expensive and may be difficult to implement in most companies, due to their complexity. In this work, the digital twin of an FMS is conceived. The specific module of an ERP (Enterprise Resources Planning) system is used to digitize the physical entity. Production data is entered according to tryouts performed in the FMS. Sensors installed in the main components of the FMS, CNC (computer numerical control) lathe, robotic arm, and pallet conveyor send information in real-time to the digital entity. The results show that simulations using the digital twin present very satisfactory results compared to the physical entity. In time, information such as production rate, queue management, feedstock, equipment, and pallet status can be easily accessed by operators and managers at any time during the production process, confirming the MES (manufacture execution system) efficiency. The low-cost hardware and software used in this work showed its feasibility. The DT created represents the initial step towards designing a metaverse solution for the manufacturing unit in question, which should operate in the near future as a smart and autonomous factory model. Full article

Service robotics involves the design of robots that work in a dynamic and very open environment, usually shared with people. In this scenario, it is very difficult for decision-making processes to be completely closed at design time, and it is necessary to define a certain variability that will be closed at runtime. MAPE-K (Monitor–Analyze–Plan–Execute over a shared Knowledge) loops are a very popular scheme to address this real-time self-adaptation. As stated in their own definition, they include monitoring, analysis, planning, and execution modules, which interact through a knowledge model. As the problems to be solved by the robot can be very complex, it may be necessary for several MAPE loops to coexist simultaneously in the robotic software architecture endowed in the robot. The loops will then need to be coordinated, for which they can use the knowledge model, a representation that will include information about the environment and the robot, but also about the actions being executed. This paper describes the use of a graph-based representation, the Deep State Representation (DSR), as the knowledge component of the MAPE-K scheme applied in robotics. The DSR manages perceptions and actions, and allows for inter- and intra-coordination of MAPE-K loops. The graph is updated at runtime, representing symbolic and geometric information. The scheme has been successfully applied in a retail intralogistics scenario, where a pallet truck robot has to manage roll containers for satisfying requests from human pickers working in the warehouse. Full article

Sustainable techniques in distribution centers, such as automation that reduces the land area needed, can be utilized. Automated Storage and Retrieval Systems (AS/RS) are used to efficiently manage the flow of pallets and carton cases in distribution centers. There are two types of AS/RS: one for pallets and another type for cases that are depalletized from pallets. Further enhancements on the system are obtained by investigating both warehouses together. This paper investigates an efficient approach that directly affects the conceptual design of automated distribution centers for the purpose of reducing the total costs. The tradeoff between the throughput (defined by the level of double handling) and warehouse capacity is investigated in this study by finding the best lot sizing rules for different classes of products (A, B, and C). These rules are to determine the method of moving carton cases from the first warehouse to the second one. The number of stacker cranes is determined based on the found throughput. The effect of double handling of pallets on the design is considered for the first time in this study. Analytical formulas and simulation were used to find the throughput and capacity based on the mentioned lot sizing rules. Then, an integer nonlinear model was developed to optimize the system. According to the results of the assumed data, the model can save up to 19.5%. The costs of stacker cranes were found to account for approximately 78.7% of the total costs in the best solution found. A decision support system has been developed to help decision makers find an efficient design of distribution center. Full article

Pallets are an important transportation tool in modern shipbuilding. With shipbuilding now trending towards larger ships, empty pallet allocation needs to meet the demands of having low costs and being sustainable for green shipbuilding. Thus, with the development of a pallet pool system, a new shipbuilding empty pallet pool allocation (SEPPA) pattern is proposed in this study. An integrated framework is developed that combines a mathematical planning model for a SEPPA pattern with a green allocation strategy. For the base case, the operation costs of the traditional shipbuilding empty pallet allocation (TSEPA) pattern and the SEPPA pattern are solved by applying an improved genetic algorithm for different pallet supply and demand situations. The results show that the SEPPA pattern is more cost-efficient than the TSEPA pattern. With increasing imbalances between supply and demand, the operation costs of the SEPPA pattern are lower than that of the TSEPA pattern. In general, the distribution of supply and demand will affect operation costs. Reasonable safety inventory intervals can reduce the operation costs of empty pallet allocation. This research may support decision making by shipbuilding pallet managers as they seek to minimize the costs of their pallet operations, by adopting practices and adapting strategies for their specific conditions. Full article

Warehouses are used to store raw materials, finished goods, defective products, tools, machinery, and other company assets until needed. In addition, the warehouse is a staging area for the storage and packaging of products delivered to the customer for consumer industries. Ideally, storage time, storage space, and delivery lead times are minimized by improving warehouse management. This study implements an integration of linear programming (LP) and decision-making models. The LP model provides decision-makers with the optimum quantity of products that can be stored in the warehouse based on different case scenarios considered in this study. Furthermore, the criteria affecting the space utilization of warehouses at total capacity are identified. An integrated approach of rough analytical hierarchical process (AHP) and rough technique for order preference by similarity to ideal solution (TOPSIS) is utilized to determine the best pallet placement on the respective rack. Additionally, this technique identifies the storage racks that require improvements in warehouse space utilization for the products. This methodological approach will help many industries and logistics teams make optimal decisions and improve productivity. Full article