Storage Location Research Articles

A review on geological storage of marine carbon dioxide: Challenges and prospects

Carbon capture, utilization, and storage (CCUS) is believed to be the most promising technology to mitigate climate change and global temperature rise. As an effective method for reducing carbon emissions, carbon dioxide (CO2) storage on the seabed can be achieved by injecting captured CO2 from offshore platforms into subsea storage sites, such as the sediment layers, depleted oil and gas reservoirs and deep saline aquifers, thereby permanently removing it from the atmosphere. Additionally, hundreds to thousands of meters of seawater above seabed storage layers provide naturally high seawater pressure and abundant barrier settings that can reduce leakage risk. Consequently, this has become an ideal storage location for long-term CO2 storage. In order to thoroughly investigate the mechanism and potential of marine CO2 storage and promote further development of CCUS, this paper provides a comprehensive description of theoretical research of marine CO2 geological storage, including storage mechanisms, storage capacity, wellbore flow, CO2 migration, cap rocks integrity and CO2 leakage monitoring. The implementation status of global engineering projects is summarized. Currently, there are relatively few successful marine storage projects globally, with the majority of others still in the research and testing stages. Finally, the challenges and prospects of marine CO2 storage are also discussed. This review demonstrates the enormous potential and promising future of marine CO2 storage, but there are also leakage risks. It can be avoided through in-depth theoretical research to clarify the entire process of storage mechanisms and take corresponding technological measures. Ultimately, a new technology for large-scale efficient injection and enhanced storage of marine CO2 storage will be formed.

Therapeutic Peptides Are Preferentially Solubilized in Specific Microenvironments within PEG-PLGA Polymer Nanoparticles.

Polymeric nanoparticles are a highly promising drug delivery formulation. However, a lack of understanding of the molecular mechanisms that underlie their drug solubilization and controlled release capabilities has hindered the efficient clinical translation of such technologies. Polyethylene glycol-poly(lactic-co-glycolic) acid (PEG-PLGA) nanoparticles have been widely studied as cancer drug delivery vehicles. In this letter, we use unbiased coarse-grained molecular dynamics simulations to model the self-assembly of a PEG-PLGA nanoparticle and its solubulization of the anticancer peptide, EEK, with good agreement with previously reported experimental structural data. We applied unsupervised machine learning techniques to quantify the conformations that polymers adopt at various locations within the nanoparticle. We find that the local microenvironments formed by the various polymer conformations promote preferential EEK solubilization within specific regions of the NP. This demonstrates that these microenvironments are key in controlling drug storage locations within nanoparticles, supporting the rational design of nanoparticles for therapeutic applications.

Optimizing Rack Locations in the Mobile-Rack Picking System: A Method of Integrating Rack Heat and Relevance

The flexible movement of racks in the mobile-rack picking system (MRPS) significantly improves the picking efficiency of e-commerce orders with the characteristics of “one order multi–items” and creates a challenging problem of how to place racks in the warehouse. This is because the placement of each rack in the MRPS directly influences the distance that racks need to be moved during order picking, which in turn affects the order picking efficiency. To handle the rack location optimization problem (RLOP), this work introduces a novel idea and methodology, taking into account the heat degree and the relevance degree of racks, to enhance the efficiency of rack placements in the MRPS. Specifically, a two-stage solution strategy is implemented. In stage 1, an integer programming model (Model 1) is developed to determine the heat and relevance degree of racks, and it can be solved quickly by the Gurobi. Stage 2 entails developing a bi-objective integer programming model (Model 2) with the objective to minimize the travel distances of robots in both heavy load and no-load conditions, using the rack heat and relevance degree as inputs. In light of the challenge of decision coupling and the vast solution space in stage 2, we innovatively propose two lower bounds by slacking off the distance between storage locations. A matheuristic algorithm based on Benders decomposition (MABBD) is designed, which utilizes Benders-related rules to reconstruct Model 2, introduces an enhanced cut and an improved optimal cut with RLOP characteristics, and designs the warm start strategy and the master variable fixed strategy. Given the substantial size of real-life problems, the Memetic algorithm (MA) is specifically devised to address them. Instances of varying sizes are also employed to validate the science and efficacy of the model and algorithm.

Temporal Paths in Real-World Sensor Networks

Sensor networks are used in an increasing number and variety of application areas, like traffic control or river monitoring. Sensors in these networks measure parameters of interest defined by domain experts and send these measurements to a central location for storage, viewing and analysis. Temporal graph data models, whose nodes contain time-series data reported by the sensors, have been proposed to model and analyze these networks in order to take informed and timely decisions on their operation. Temporal paths are first-class citizens in this model and some classes of them have been identified in the literature. Queries aimed at finding these paths are denoted as (temporal) path queries. In spite of these efforts, many interesting problems remain open and, in this work, we aim at answering some of them. More concretely, we characterize the classes of temporal paths that can be defined in a sensor network in terms of the well-known Allen’s temporal algebra. We also show that, out of the 8192 possible interval relations in this algebra, only 11 satisfy two desirable properties that we define: transitivity and robustness. We show how these properties and the paths that satisfy them are relevant in practice by means of a real-world use case consisting of an analysis of salinity that appears close to the Scheldt river in Flanders, Belgium, during high tides occurring in the North Sea.

Exploring the performance impact of unit load selection in order picking: evidence from a cold retail supply chain

PurposePrior literature has widely established that the design of storage locations impacts order picking task performance. The purpose of this study is to investigate the performance impact of unit loads, e.g. pallets or rolling cages, utilized by pickers to pack products after picking them from storage locations.Design/methodology/approachAn empirical analysis of archival data on a manual order picking system for deep-freeze products was performed in cooperation with a German brick-and-mortar retailer. The dataset comprises N = 343,259 storage location visits from 17 order pickers. The analysis was also supported by the development and the results of a batch assignment model that takes unit load selection into account.FindingsThe analysis reveals that unit load selection affects order picking task performance. Standardized rolling cages can decrease processing time by up to 8.42% compared to standardized isolated rolling boxes used in cold retail supply chains. Potential cost savings originating from optimal batch assignment range from 1.03% to 39.29%, depending on batch characteristics.Originality/valueThis study contributes to the literature on factors impacting order picking task performance, considering the characteristics of unit loads where products are packed on after they have been picked from the storage locations. In addition, it provides potential task performance improvements in cold retail supply chains.

Simulasi Proses Picking Order dengan Metode Dedicated Storage Menggunakan Software Fleksim (Studi Kasus PT XYZ)

PT XYZ is a manufacturing company that produces food and drinks by providing quality products that are halal, high quality and safe for consumption by the public. In producing products, companies must provide several adequate facilities to support production activities, one of which is warehouse facilities. Currently, warehouses where finished food products are stored are stored randomly or placed in empty areas without paying attention to product entry and exit activities. This condition causes picking activities to take a long time and makes stock monitoring difficult. The aim of this research is to produce an appropriate goods storage design so that it can increase warehouse effectiveness. This research uses a special storage method. Where a special storage policy or method is permanently placed on each item. The number of product storage locations must meet the maximum storage space requirements and a simulation is carried out using FlexSim software. The research results show that designing goods storage using the dedicated storage method results in a reduction in the distance traveled for the collection process reaching 52%.

Clinical Needs Assessment of a Machine Learning-Based Asthma Management Tool: User-Centered Design Approach.

Personalized asthma management depends on a clinician's ability to efficiently review patient's data and make timely clinical decisions. Unfortunately, efficient and effective review of these data is impeded by the varied format, location, and workflow of data acquisition, storage, and processing in the electronic health record. While machine learning (ML) and clinical decision support tools are well-positioned as potential solutions, the translation of such frameworks requires that barriers to implementation be addressed in the formative research stages. We aimed to use a structured user-centered design approach (double-diamond design framework) to (1) qualitatively explore clinicians' experience with the current asthma management system, (2) identify user requirements to improve algorithm explainability and Asthma Guidance and Prediction System prototype, and (3) identify potential barriers to ML-based clinical decision support system use. At the "discovery" phase, we first shadowed to understand the practice context. Then, semistructured interviews were conducted digitally with 14 clinicians who encountered pediatric asthma patients at 2 outpatient facilities. Participants were asked about their current difficulties in gathering information for patients with pediatric asthma, their expectations of ideal workflows and tools, and suggestions on user-centered interfaces and features. At the "define" phase, a synthesis analysis was conducted to converge key results from interviewees' insights into themes, eventually forming critical "how might we" research questions to guide model development and implementation. We identified user requirements and potential barriers associated with three overarching themes: (1) usability and workflow aspects of the ML system, (2) user expectations and algorithm explainability, and (3) barriers to implementation in context. Even though the responsibilities and workflows vary among different roles, the core asthma-related information and functions they requested were highly cohesive, which allows for a shared information view of the tool. Clinicians hope to perceive the usability of the model with the ability to note patients' high risks and take proactive actions to manage asthma efficiently and effectively. For optimal ML algorithm explainability, requirements included documentation to support the validity of algorithm development and output logic, and a request for increased transparency to build trust and validate how the algorithm arrived at the decision. Acceptability, adoption, and sustainability of the asthma management tool are implementation outcomes that are reliant on the proper design and training as suggested by participants. As part of our comprehensive informatics-based process centered on clinical usability, we approach the problem using a theoretical framework grounded in user experience research leveraging semistructured interviews. Our focus on meeting the needs of the practice with ML technology is emphasized by a user-centered approach to clinician engagement through upstream technology design.

The History and Management of the Materials of the Kuomintang People Such as Huang Fu-Focusing On American Archival Institution

Huang Fu was important in the political history of the Republic of China. But regarded as a pro-Japanese "traitor", there are few studies on him in China and most of his original materials aren't in mainland China. Against this background, this article focuses on Huang Fu. It determines the collection and utilization of his materials by analyzing books, papers and references. It also introduces in detail the collection information of the Hoover Institution at Stanford University and the Rare Book and Manuscript Library at Columbia University based on archive books and official website information. It aims to provide material storage locations for modern and contemporary history researchers to promote in-depth research on Huang Fu and other Kuomintang figures in the Republic of China period.

Improving the picking efficiency of a cold warehouse to avoid temperature abuse

PurposeThis research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.Design/methodology/approachThe authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.FindingsAll the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.Research limitations/implicationsThe results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.Practical implicationsA storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.Originality/valuePrevious research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

Redesign of the temporary storage facilities for hazardous and toxic waste at PT X

PT X is an industry engaged in the production of snacks. The processes and activities in the industry produce by-products, namely hazardous and toxic waste. All waste is stored in temporary storage, but based on observations, the extant temporary storage does not meet the government's requirements. Accordingly, PT X redesigned a temporary storage area following government requirements. This study aims to identify a temporary storage location that complies with government regulations for PT X. Recording the types, characteristics, and quantities produced by PT X, followed by existing observation, is followed by the calculation of facility needs according to SNI and individual calculations. This research analysis provides property and layout recommendations to enhance the condition of PT X's temporary storage for hazardous and toxic refuse.

FORMATION OF PRODUCT ASSORTMENT OF RETAIL ENTERPRISES

The article examines the peculiarities of the formation of the product range of retail trade. An optimal product range was formed in order to increase consumer loyalty to the company's products. In the article have been defined that in the direction to ensure consumer loyalty to the company, first of all, the optimal assortment should be formed and, accordingly, the formation of goods should be carried out. Namely, the application of the integrated ABC/FMR analysis of the product range allows you to classify product stocks according to the criteria of "profitability of product sales" and "level of demand», and will contribute to the formation of an optimal product range which helps to ensure the rhythmic work of the trading company and serves as the basis for achieving the target indicators of economic activity. The application of the integrated ABC/FMR analysis of the product range made it possible to classify the product stocks according to such criteria as: "profitability of product sales" and "level of demand", which, thereby, will contribute to the formation of the optimal product range, ensuring a high level of efficiency of the trading enterprise. The proposed approach to structuring the product range, which is based on the results ofккня ABC/FMR analysis, makes it possible to classify the entire set of product units in the selected product line into nine subgroups. It also has been identified that in sales management, the "degree of demand" of a product or the requests frequency for certain groups of products serves as an important criterion for positioning product groups in the marketing strategy of management (the slogan "we have them all"). In goods management, FMR analysis is used to determine the location of goods storage, so the most frequently requested ("fast" - fast) positions are located closer to the assembly areas.

Creating saponin-free yellow pea seeds by CRISPR/Cas9-enabled mutagenesis on β-amyrin synthase.

Dry pea (Pisum sativum) seeds are valuable sources of plant protein, dietary fiber, and starch, but their uses in food products are restricted to some extent due to several off-flavor compounds. Saponins are glycosylated triterpenoids and are a major source of bitter, astringent, and metallic off-flavors in pea products. β-amyrin synthase (BAS) is the entry point enzyme for saponin biosynthesis in pea and therefore is an ideal target for knock-out using CRISPR/Cas9 genome editing to produce saponin deficient pea varieties. Here, in an elite yellow pea cultivar (CDC Inca), LC/MS analysis identified embryo tissue, not seed coat, as the main location of saponin storage in pea seeds. Differential expression analysis determined that PsBAS1 was preferentially expressed in embryo tissue relative to seed coat and was selected for CRISPR/Cas9 genome editing. The efficiency of CRISPR/Cas9 genome editing of PsBAS1 was systematically optimized in pea hairy roots. From these optimization procedures, the AtU6-26 promoter was found to be superior to the CaMV35S promoter for gRNA expression, and the use of 37°C was determined to increase the efficiency of CRISPR/Cas9 genome editing. These promoter and culture conditions were then applied to stable transformations. As a result, a bi-allelic mutation (deletion and inversion mutations) was generated in the PsBAS1 coding sequence in a T1 plant, and the segregated psbas1 plants from the T2 population showed a 99.8% reduction of saponins in their seeds. Interestingly, a small but statistically significant increase (~12%) in protein content with a slight decrease (~5%) in starch content was observed in the psbas1 mutants under phytotron growth conditions. This work demonstrated that flavor-improved traits can be readily introduced in any pea cultivar of interest using CRISPR/Cas9. Further field trials and sensory tests for improved flavor are necessary to assess the practical implications of the saponin-free pea seeds in food applications.

Analysis of Hydrogen Energy Storage Location and Capacity Determination and Power Grid Planning Suitable for Renewable Energy Large-Scale Development

Analysis of Hydrogen Energy Storage Location and Capacity Determination and Power Grid Planning Suitable for Renewable Energy Large-Scale Development

Optimization Strategy for Storage Location Allocation in Air Cargo Warehouse Area

Facing rapid growth in air cargo demand within the global supply chain and limitations of traditional storage location allocation methods, this study proposes an intelligent optimization strategy for storage location allocation. The strategy considers key factors like flight schedules, cargo collection and delivery data, cargo terminal topology, and in-and-out paths. It intelligently allocates each cargo batch to the most suitable location, assigning more flights' cargo to locations near the exit, thereby reducing cargo handling distance and offloading time, and enhancing operational efficiency and cargo handling capacity in the air freight sector. The algorithm, based on the latest optimization theories and techniques, seeks optimal solutions under various constraints. The model and algorithm’s effectiveness is validated through simulation experiments using actual flight data. The results indicate that, compared to traditional strategies, the optimized strategy reduces response time by 62%.

The Variance Learning Curve in Retail Order Picking

The notion that prior experience can improve future performance has reached general acceptance in the operations and supply chain management literature. However, prior experience may not only have a positive impact on average performance but in addition also on the variance of performance. We empirically examine this issue by considering the performance of 19 temporary workers in retail order picking new to this task, performing 22,603 storage location visits and use a two-stage estimation method of a heteroskedastic learning curve model. In the first stage, we estimate the impact of prior experience on task performance time. For the second stage, we extract the error terms from the first stage model and use it as the dependent variable in the second stage. Through this, we can estimate the impact of prior experience on performance variability. Our first stage model suggests that prior experience can improve average task performance on a system level for all new temporary order pickers observed. However, on an individual level, these learning curves are heterogeneous, with varying intercepts, slopes, and functional forms. Going beyond the impact of experience on average performance, our second stage model finds that one week of experience working in the examined warehouse can reduce performance variability by 26%, but at diminishing increments. These findings suggest that prior experience can mitigate variability in task performance time, which indicates that the recently discussed variance learning curve exists for order picking tasks. Our results allow managers to make informed decisions on how to benefit from the positive effects of prior experience on average performance and performance variability when employing a temporary workforce for the example case of retail warehouse order picking.

Machine Learning in Warehouse Management: A Survey

Warehouse design and planning involve complex decisions on receiving, storage, order picking and shipping products (i.e., stock-keeping units - SKUs) and can affect the performance of entire supply chains. With the advancement of Industry 4.0 and increased data availability, high-computing power, and ample storage capacity, Machine Learning (ML) has become an appealing technology to address warehouse planning challenges such as Storage Location Assignment Problems (SLAP) and Order Picking Problems (OPP) for intelligent warehousing management. This paper presents a state-of-the-art review of ML applied to Warehouse Management Systems (WMS) through the analysis of recent research application articles. A mapping to classify the scientific literature in this new research area, including ML methods, algorithms, data sources and use cases of ML-aided WMS, as well as further research perspectives and challenges, are introduced. Preliminary results suggest that the possible research areas in ML-WMS are still incipient and need to be further explored.

Optimization Management of Storage Location in Stereoscopic Warehouse by Integrating Genetic Algorithm and Particle Swarm Optimization Algorithm

The management of a three‐dimensional warehouse is a key part of the upper computer monitoring and management system for three‐dimensional warehouses. Currently, there are problems such as unreasonable planning and inability to respond to real‐time demands. Therefore, further improvement is needed to optimize the management of storage locations. The main purpose of the research is to achieve better storage stability and improve storage and retrieval efficiency. First, the study constructed a multiobjective mathematical model based on the weight, frequency of use, and category of the goods. Three objective functions were constructed. Therefore, the operational efficiency of the stacker crane and the turnover rate of items were improved. Meanwhile, the overall stability of the shelves was ensured, and the management efficiency of the warehouse was improved. At the same time, the study introduced the GA‐PSO algorithm to solve the mathematical model and optimize the goods location planning. These results confirmed that the proposed algorithm had significantly lower iteration times than traditional particle swarm optimization in different warehouse sizes and types of goods. The iteration required to reach the optimal value in Situation 1 was only 80, which was 90 fewer than PSO. Meanwhile, in Situation 2, the optimization results of the proposed algorithm in four objective functions were as high as 42.94%, 26.03%, 30.72%, and 46.15%, respectively, which increased by 1.20%, 8.04%, 5.61%, and 7.38% compared to PSO. The proposed algorithm can achieve more efficient and intelligent warehouse management, improving the efficiency and accuracy of logistics operations. It is significant for logistics industry development and enterprise competitiveness enhancement.

Work Practice Diversity in Enterprise Collaboration Systems: an Analysis of Social Documents

Enterprise collaboration systems are open to interpretive flexibility, resulting in a multitude of approaches to achieving the same task. This exploratory study provides insights into the diverse ways collaboration system professionals use collaboration technology. Data was collected about the systems, applications and type of documents they use for the typical work scenario of preparing and documenting online team meetings. The collected data was translated into visual representations of the collaborative work that happens around social documents. The results of the study reveal that there is no uniform approach and that the information involved in this process is not stored in a defined location but distributed across different systems, spaces and containers. Whilst there are observable similarities in the way employees carry out the scenario, the detailed steps are different for each case. Even within the same organisation there are fewer similarities than might be expected. The fact that the information relating to the same collaboration scenario is distributed across different storage locations reveals potential problems from an information management perspective with regards to information retrievability and accessibility.

Cold Storage Order Picking Performance: Effects of Load Unit Utilization and Product Volume

Increasing labor costs in retail have intensified the focus on order-fulfillment performance, particularly in warehouse operations. In this context, manual picker-to-parts systems, especially in cold storage environments processing deep-frozen products below -20°C, are of significant interest. Our study explores order picking in such cold storage environments, where products are packed into insulated, roll containers serving as load units. While existing research has explored the effects of product characteristics such as weight, volume, and different load units like pallets from a theoretical perspective, the specific interplay between load unit utilization levels and product volume remains underexplored in practical settings. To address this gap, we conducted a field study focusing on a cold storage of a prominent German brick-and-mortar grocery retailer and analyzed a comprehensive longitudinal dataset comprising 227,274 storage location visits by 29 order pickers in January 2023. Employing a mixed-effects model, we examined the impact of these factors on order picking time (OPT). Our findings revealed a subtle relationship: a 1% increase in load unit utilization level correlates with a 1% increase in OPT, signifying that higher utilization levels negatively impact pickers’ performance. Notably, our analysis also indicated that product volume moderates this effect. Specifically, lower product volumes can mitigate the adverse effects of high load unit utilization, whereas higher product volumes amplify these effects. This research offers valuable insights for warehouse managers, particularly in cold storage settings on finding the balance between OPT and load unit utilization.

Enhancing Warehouse Efficiency With Time Series Clustering: A Hybrid Storage Location Assignment Strategy

Enhancing Warehouse Efficiency With Time Series Clustering: A Hybrid Storage Location Assignment Strategy