The international conference on Data Warehousing and Knowledge Discovery (DaWaK) has become a pivotal place to exchange experiences and knowledge among researchers and practitioners in big data ...analytics. The conference has been essential to data warehousing and data analytics for the last 21 years (1999–2019). This study explored the knowledge structure embedded in the DaWaK Conference papers and examined the research trends over time. It also analyzed the performance of published papers, authors, and their affiliations and countries and visualized a collaboration network in DaWaK. We applied several text mining techniques, including co-word analysis, topic modeling, co-author network analysis, and network visualization. The study’s findings indicate that the core topics are data mining techniques, algorithm performance, and information systems. The popular topic trends are associated with database encryption, whereas the topics related to online analytical processing (OLAP) technology are in decline. The research metrics results demonstrate that the DaWaK papers were cited 6,262 times, with an h-index of 34 for the 722 DaWaK papers. The article titled “Outlier Detection Using Replicator Neural Networks” reached the most citations (177), and the most productive author was Bellatreche, Ladjel (15 papers). Nanyang Technological University is the most frequently mentioned as the author’s affiliation, the United States is the country with the largest number of authors, and the National Science Foundation was the largest funding agency that supported the DaWaK researchers. Moreover, the authorship network of Bellatreche, Ladjel is the largest collaboration network in the DaWaK scholar community. The outcomes of this study would be beneficial for comprehending the knowledge in data warehousing and the relevant cross-disciplinary areas of research and collaboration networks in this field.
There has been considerable research on the environmental impact of supply chains but most of this has concentrated on the transport elements. The environmental impact of warehousing has received ...relatively little attention except within the context of distribution networks. A high proportion of total warehouse emissions emanate from heating, cooling, air conditioning and lighting and these aspects are largely related to warehouse size. This in turn is greatly influenced by inventory management, affecting stockholding levels, and warehouse design, affecting the footprint required for holding a given amount of stock. Other emissions, such as those caused by material handling equipment, are closely related to warehouse throughput and equipment choice. There is a substantial gap in the literature regarding this interaction between inventory and warehouse management and its environmental impact. The purpose of this paper is to contribute to filling this gap. Therefore, an integrated simulation model has been built to examine this interaction and the results highlight the key effects of inventory management on warehouse-related greenhouse gas emissions. In particular, it is found that decisions on supply lead times, reorder quantities, and storage equipment all have an impact on costs and emissions and therefore this integrated approach will inform practical decision making. Additionally, it is intended that the paper provides a framework for further research in this important area.
A materialized view selection in data warehouse management is important to speed up query processing. The data presented in data warehouses is generally stored as a set of materialized views. The ...major challenge is determining which views to materialize and satisfy the response time with reduced cost functions. This paper proposed an effective multi objective cost model based flamingo search algorithm for materialized view selection in data warehouse design. The multiple view processing plan structure of the data warehouse describes the search space of problem in order to select the optimal materialized views. The proposed model evaluates a multi‐objective optimization problem based on the cost functions resulting from materialization. The multiple objective functions of the proposed model are maintenance costs, current query processing costs, response cost and previous query processing costs. This model selects the top‐k views for materialization by satisfying the mentioned multi‐objective functions. The experimental results are simulated using the TPC‐H dataset. The efficacy of proposed model is measured by comparing the obtained results of the proposed model with various existing approaches.
In this paper, we deal with the sequencing and routing problem of order pickers in conventional multi-parallel-aisle warehouse systems. For this NP-hard Steiner travelling salesman problem (TSP), ...exact algorithms only exist for warehouses with at most three cross aisles, while for other warehouse types literature provides a selection of dedicated construction heuristics. We evaluate to what extent reformulating and solving the problem as a classical TSP leads to performance improvements compared to existing dedicated heuristics. We report average savings in route distance of up to 47% when using the LKH (Lin–Kernighan–Helsgaun) TSP heuristic. Additionally, we examine if combining problem-specific solution concepts from dedicated heuristics with high-quality local search features could be useful. Lastly, we verify whether the sophistication of ‘state-of-the-art’ local search heuristics is necessary for routing order pickers in warehouses, or whether a subset of features suffices to generate high-quality solutions.
About This BookLearn Redmine through the basic topics to the mastering onesCustomize Redmine without breaking upgrade compatibilityBecome an expert of Redmine after having read this comprehensive ...guide with tips, tricks and best practices.Who This Book Is For This book is best suited for project managers and Redmine administrators who have a working knowledge of Redmine and who want to get advanced practical knowledge in order to manage and monitor projects effectively and efficiently. What You Will LearnForget about having trouble installing and configuring RedmineFeel at ease with using Redmine wiki syntaxGet familiar with the permissions system and issue life cycle in RedmineUse Redmine for issue tracking, project hosting, project management, and time trackingFind and choose plugins, and get familiar with some of the most useful Redmine pluginsIn Detail This book is an update of our successful previous edition, Mastering Redmine. It is a comprehensive guide that will give you a detailed practical understanding of how to effectively manage, monitor, and administer complex projects using Redmine. You will become familiar with the concept of issue tracking and will get to know why and what makes Redmine one of the best issue trackers. Project management capabilities of Redmine will show why this is one of the best applications for project hosting. Furthermore, you will learn about Redmine rich text formatting syntax, access control, workflows, and time tracking. Toward the end of the book, you will unleash the power of custom fields and will get to know how to customize Redmine without breaking upgrade compatibility. By the end of the book, you will have a deep practical understanding of how to effectively monitor and manage large scale and complex projects using Redmine.
A Robotic Mobile Fulfillment System is a recently developed automated, parts-to-picker material handling system. Robots can move storage shelves, also known as inventory pods, between the storage ...area and the workstations and can continually reposition them during operations. This article shows how to optimize three key decision variables: (i) the number of pods per SKU; (ii) the ratio of the number of pick stations to replenishment stations; and (iii) the replenishment level per pod. Our results show that throughput performance improves substantially when inventory is spread across multiple pods, when an optimum ratio between the number of pick stations to replenishment stations is achieved and when a pod is replenished before it is completely empty. This article contributes methodologically by introducing a new type of Semi-Open Queueing Network (SOQN): cross-class matching multi-class SOQN, by deriving necessary stability conditions, and by introducing a novel interpretation of the classes.
•We build performance estimation models to explicitly investigate battery recovery.•We develop a decomposition method to analyze models with two synchronization nodes.•Ignoring battery recovery ...underestimates the number of robots and the system costs.•Inductive charging performs the best in terms of retrieval throughput time.•Battery swapping is cheaper than plug-in charging when battery costs are low.
Robotic mobile fulfillment systems (RMFS) have seen many implementations in recent years, due to their high flexibility and low operational cost. Such a system stores goods in movable shelves and uses movable robots to transport the shelves. The robot is battery powered and the battery depletes during operations, which can seriously affect the performance of the system. This study focuses on battery management problem in an RMFS, considering a battery swapping and a battery charging strategy with plug-in or inductive charging. We build a semi-open queueing network (SOQN) to estimate system performance, modeling the battery charging process as a single queue and the battery swapping process as a nested SOQN. We develop a decomposition method to solve the analytical models and validate them through simulation. Our models can be used to optimize battery recovery strategies and compare their cost and throughput time performance. The results show that throughput time performance can be significantly affected by the battery recovery policy, that inductive charging performs best, and that battery swapping outperforms plug-in charging by as large as 4.88%, in terms of retrieval transaction throughput time. However, the annual cost of the RMFS using the battery swapping strategy is generally higher than that of the RMFS using the plug-in charging strategy. In the RMFS that uses the inductive charging strategy, a critical price of a robot can be found, for a lower robot price and a small required retrieval transaction throughput time, inductive charging outperforms both plug-in charging and battery swapping strategies in terms of annual cost. We also find that ignoring the battery recovery will underestimate the number of robots required and the system cost for more than 15%.
Clinical publications use mortality as a hard end point. It is unknown how many patient deaths are under-reported in institutional databases. The objective of this study was to query mortality in our ...patient cohort from our data warehouse and compare these deaths to those identified in different databases.
We passed the first/last name and date of birth of 134 patients through online mortality search engines (Find a Grave Index, US Cemetery and Funeral Home Collection, etc.) to assess their ability to capture patient deaths and compared that to deaths recorded from our institutional data warehouse.
Our institutional data warehouse found approximately one-third of the total patient mortalities. After the Social Security Death Index, we found that the Find a Grave Index captured the most mortalities missed by the institutional data warehouse. These results highlight the advantages of incorporating readily available search engines into institutional data warehouses for the accurate collection of patient mortalities, particularly those that occur outside of index operative admission.
The incorporation of the mortality search engines significantly augmented the capture of patient deaths. Our approach may be useful for tailored patient outreach and reporting mortalities with institutional data.
•Scheduling mechanism concerning a Kiva system.•Integrated sequencing of orders and racks visits.•Allows to cut fleet size by 50%.•Investigation of impact of the shared storage policy.
This paper ...treats a special parts-to-picker based order processing system, where mobile robots hoist racks and bring them directly to stationary pickers. This technological innovation – known as the Kiva system – heavily influences all traditional planning problems to be solved when operating a warehouse. We, specifically, tackle the order processing in a picking station, i.e., the batching and sequencing of picking orders and the interdependent sequencing of the racks brought to a station. We formalize the resulting decision problem and provide suited solution procedures. In a comprehensive computational study we show that an optimized order picking allows to more than halve the fleet of robots compared to simple decision rules often applied in real-world warehouses.
PurposeOrganisations and industries are often looking for technologies that can accomplish multiple tasks, providing economic benefits and an edge over their competitors. In this context, drones have ...the potential to change many industries by making operations more efficient, safer and more economic. Therefore, this study investigates the use of drones as the next step in smart/digital warehouse management to determine their socio-economic benefits.Design/methodology/approachThe study identifies various enablers impacting drone applications to improve inventory management, intra-logistics, inspections and surveillance in smart warehouses through a literature review, a test of concordance and the fuzzy Delphi method. Further, the graph theory matrix approach (GTMA) method was applied to ranking the enablers of drone application in smart/digital warehouses. In the subsequent phase, researchers investigated the relation between the drone application's performance and the enablers of drone adoption using logistic regression analysis under the TOE framework.FindingsThis study identifies inventory man agement, intra-logistics, inspections and surveillance are three major applications of drones in the smart warehousing. Further, nine enablers are identified for the adoption of drone in warehouse management. The findings suggest that operational effectiveness, compatibility of drone integration and quality/value offered are the most impactful enablers of drone adoption in warehouses. The logistic regression findings are useful for warehouse managers who are planning to adopt drones in a warehouse for efficient operations.Research limitations/implicationsThis study identifies the enablers of drone adoption in the smart and digital warehouse through the literature review and fuzzy Delphi. Therefore, some enablers may be overlooked during the identification process. In addition to this, the analysis is based on the opinion of the expert which might be influenced by their field of expertise.Practical implicationsBy considering technology-organisation-environment (TOE) framework warehousing companies identify the opportunities and challenges associated with using drones in a smart warehouse and develop strategies to integrate drones into their operations effectively.Originality/valueThis study proposes a TOE-based framework for the adoption of drones in warehouse management to improve the three prominent warehouse functions inventory management, intra-logistics, inspections and surveillance using the mixed-method.
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