Engineering Commons^™

Grouping Techniques To Manage Large-Scale Multi-Item Multi-Echelon Inventory Systems, Anvar Abaydulla

Graduate Theses and Dissertations

Large retail companies operate large-scale systems which may consist of thousands of stores. These retail stores and their suppliers, such as warehouses and manufacturers, form a large-scale multi-item multi-echelon inventory supply network. Operations of this kind of inventory system require a large number of human resources, computing capacity, etc.

In this research, three kinds of grouping techniques are investigated to make the large-scale inventory system “easier” to manage. The first grouping technique is a network based ABC classification method. A new classification criterion is developed so that the inventory network characteristics are included in the classification process, and this criterion …

Efficient Algorithms For Clustering Polygonal Obstacles, Sabbir Kumar Manandhar

UNLV Theses, Dissertations, Professional Papers, and Capstones

Clustering a set of points in Euclidean space is a well-known problem having applications in pattern recognition, document image analysis, big-data analytics, and robotics. While there are a lot of research publications for clustering point objects, only a few articles have been reported for clustering a given distribution of obstacles. In this thesis we examine the development of efficient algorithms for clustering a given set of convex obstacles in the 2D plane. One of the methods presented in this work uses a Voronoi diagram to extract obstacle clusters. We also consider the implementation issues of point/obstacle clustering algorithms.

Unsupervised Learning Framework For Large-Scale Flight Data Analysis Of Cockpit Human Machine Interaction Issues, Abhishek B. Vaidya

Open Access Theses

As the level of automation within an aircraft increases, the interactions between the pilot and autopilot play a crucial role in its proper operation. Issues with human machine interactions (HMI) have been cited as one of the main causes behind many aviation accidents. Due to the complexity of such interactions, it is challenging to identify all possible situations and develop the necessary contingencies. In this thesis, we propose a data-driven analysis tool to identify potential HMI issues in large-scale Flight Operational Quality Assurance (FOQA) dataset. The proposed tool is developed using a multi-level clustering framework, where a set of basic …