Joint application of radio-frequency identification (RFID) and ultrawideband (UWB) technologies in the intelligent warehousing management system is proposed. In this system, we regard forklift as ...infrastructure, and both the UWB mobile terminal (MT) and the RFID reader are mounted on the forklift. The RFID reader is used not only to read the information of goods but also to determine the goods' status of loading and unloading. The UWB MT is used to locate the forklift. The goods or pallets are labeled with RFID tags. Utilizing the integration of these two technologies, the dual goals of goods information and goods location perception are achieved. An <inline-formula> <tex-math notation="LaTeX">M/N </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> sliding window method is proposed to determine the loading and unloading of goods in this article. Our experiments reveal that this novel method can quickly, accurately, and efficiently determine the states of the goods on the forklift. For the indoor localization, an algorithm is proposed based on RSS residual weighting (RRW). Experiments show that RRW can mitigate the nonline-of-sight error substantially compared with the conventional Taylor algorithm and recent Convex approximation algorithm. Finally, a real working practice in a warehouse of a company is introduced, and it illustrates the feasibility of the system.
In the last few decades, precision medicine became a highly weighted subject as the risk of side effects grew with the increasing drug use. The mass of evolving pharmacogenetic data invites to ...establish a preemptive genotyping approach and the integration of those molecular data into the workflow of an existing hospital information system to improve the safety of drug therapy. The newly introduced software module GraphSAW2-DWHBuilder creates the graph-based data warehouse. For that, molecular data from various sources is assembled in a graph database. Furthermore, an update strategy was developed to ensure that only the latest version of the data is used. To eliminate redundancies and to guarantee efficient querying, a mapper strategy was established. Another new module named GraphSAW2-Check is embedded in an existing drug therapy safety workflow used in hospitals. It contains methods for the pharmacogenetic check, which access the molecular data warehouse of the GraphSAW2-DWHBuilder and execute queries on the graph. These queries contain the drugs and the molecular data of interest and search for connections that represent toxic associations between them. If a connection is found, a warning will be issued to the medical practitioner, who can then reconsider their prescription. The results reveal the added value of molecular data integration to the drug therapy safety.
Robotisation is increasing in warehouse operations, but human employment continues to be relevant. Traditionally manual activities, such as order picking, are being re-designed into collaborative ...human-robot tasks. This trend exemplifies the transition towards a human-centric Industry 5.0, focusing on synergy instead of seeking human replacement. However, human workers are increasingly hard to recruit and retain. We contribute to the underrepresented literature on human factors within the domain of operations and production management research and investigate the deployment of robotic technologies alongside human workers in a sustainable way. With a unique real-effort experiment, we investigate how the manipulation of picker's experienced levels of autonomy affects their job satisfaction and core self-evaluations, two key behavioural outcomes that determine employee turnover intentions. We establish that the introduction of human-robot collaboration positively affects job satisfaction for the contrasting collaboration dynamics of (i) gaining control (the human leading the robot) and (ii) ceding control (the human following the robot). This positive effect is larger when the human is following the robot. We additionally find that following the robot positively affects pickers' self-esteem and that self-efficacy related to human-robot interaction benefits from the introduction of collaborative robotics, regardless of the setup dynamics.
Decisions made for designing and operating a warehouse system are of great significance. These operational decisions are strongly affected by total logistics costs, including investment and direct ...operating costs. The number of orders made by customers in the logistics section of warehouse management is very high because the number, type of products and items ordered by different customers vary broadly. However, machines layout for picking up products at logistics centres is minimal, inflexible, and, in some cases, inconclusive. In this study, we address joint order batching procedures of orders considering picker routing problem as a mixed-integer programming model. Extensive numerical experiments were generated in small, medium, and large sizes. In order to consider the uncertainty of parameters, we applied robust possibilistic programming for this problem. Three different meta-heuristic algorithms; genetic algorithm, particle swarm optimisation algorithm, and honey artificial bee colony algorithms are used as solution approaches to solve the formulated model. The performance of solution approaches over the problem was analysed using several test indexes. In all three group examples, there was no significant difference among mean values of the objective function, while there was a remarkable difference among computing times.
Querying and reporting from large volumes of structured, semistructured, and unstructured data often requires some flexibility. This flexibility provided by fuzzy sets allows for categorization of ...the surrounding world in a flexible, human-mind-like manner. Apache Hive is a data warehousing framework working on top of the Hadoop platform for big data processing. Hive allows executing queries and aggregating and analyzing data stored in Hadoop distributed file system and other repositories. Hive responds to the current needs for efficient big data warehousing, which is impossible with traditional data warehouses due to their rigid nature. This article presents the FuzzyHive library that extends the Hive framework with fuzzy sets based techniques for querying, analyzing, and reporting on big data warehouses. We formalize the fuzzy techniques used while operating on Hive-based data warehouses (including fuzzy filtering on dimensional attributes, projection with fuzzy transformation, fuzzy grouping, and joining). We also show how we embedded these operations in Hive query language, which was not studied so far. Such extensions make big data warehousing more flexible and contribute to the portfolio of tools used by the community of people working with fuzzy sets and data analysis. The FuzzyHive library complements the spectrum of available solutions for fuzzy data processing and querying in large datasets. We investigate Hive fuzzy querying performance, effectiveness, and scalability for various data storage formats (text, Avro, and Parquet). Our experiments demonstrate that the proposed extensions introduce more elasticity and are also efficient for big data warehousing, which is the first such kind of solution for this environment.
•Study of the clustered generalized traveling salesman problem.•Propose an algorithm for transforming the problem to the traveling salesman problem.•Discussion of relevant problems in logistics, ...robotic warehousing and drone delivery.•Computational study by randomly-generated and practice-oriented instances.•Algorithm is shown highly efficient compared to existing methods.
The clustered generalized traveling salesman problem (CGTSP) is an extension of the classical traveling salesman problem (TSP), where the set of nodes is divided into clusters of nodes, and the clusters are further divided into subclusters of nodes. The objective is to find the minimal route that visits exactly one node from each subcluster in such a way that all subclusters of each cluster are visited consecutively. Due to the additional flexibility of the CGTSP compared to the classical TSP, CGTSP can incorporate a wider range of complexities arising from some practical applications. However, the absence of a good solution method for CGTSP is currently a major impediment in the use of the framework for modeling. Accordingly, the main objective of this paper is to enable the powerful framework of CGTSP for applied problems. To attain this goal, we first develop a solution method by an efficient transformation from CGTSP to TSP. We then demonstrate that not only the solution method provides far superior solution quality compared to existing methods for solving CGTSP, but also it enables practical solutions to far larger CGTSP instances. Finally, to illustrate that the modeling framework and the solution method apply to some practical problems of realistic sizes, we conduct a computational experiment by considering the application of CGTSP to two modern logistics problems; namely, automated storage and retrieval systems (logistics inside the warehouse) and drone-assisted parcel delivery service (logistics outside the warehouse).
Quickly build and deploy massive data pipelines and improve productivity using Azure Databricks Key Features * Get to grips with the distributed training and deployment of machine learning and deep ...learning models * Learn how ETLs are integrated with Azure Data Factory and Delta Lake * Explore deep learning and machine learning models in a distributed computing infrastructure Book Description Microsoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you'll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you'll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you'll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you'll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline. What you will learn * Create ETLs for big data in Azure Databricks * Train, manage, and deploy machine learning and deep learning models * Integrate Databricks with Azure Data Factory for extract, transform, load (ETL) pipeline creation * Discover how to use Horovod for distributed deep learning * Find out how to use Delta Engine to query and process data from Delta Lake * Understand how to use Data Factory in combination with Databricks * Use Structured Streaming in a production-like environment Who this book is for This book is for software engineers, machine learning engineers, data scientists, and data engineers who are new to Azure Databricks and want to build high-quality data pipelines without worrying about infrastructure. Knowledge of Azure Databricks basics is required to learn the concepts covered in this book more effectively. A basic understanding of machine learning concepts and beginner-level Python programming knowledge is also recommended.
During the COVID-19 pandemic, e-commerce retailers have had trouble satisfying the growing demand because of limited warehouse capacity constraints. Fortunately, an on-demand warehousing system has ...emerged as a new alternative to mitigate warehouse capacity issues. In recent years, several studies have focused on the supply chain problem considering on-demand warehousing. However, there is no study that deals simultaneously with inherent uncertainties and the property of commitment, which is the main advantage of on-demand warehousing. To fill these research gaps, this paper presents an e-commerce supply chain network design problem considering an on-demand warehousing and decisions for commitment periods. We propose the two-stage stochastic programming model that captures the inherent uncertainties to formulate the presented problem. We solve the proposed model utilizing sample average approximation combined with the Benders decomposition algorithm. Of particular note, we develop a method to generate effective initial cuts for improving the convergence speed of the Benders decomposition algorithm. Computational results show that the developed method could find an effective feasible solution within a reasonable computational time for problems of practical size. Furthermore, we show the significant cost-saving effects, based on experiment results, that occur when an on-demand warehousing system is used for designing supply chain networks.
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