3D Visualization of a Case-Based Distance Model

Regarding to the conveyance of materials the assignment of stock-keeping units (SKUs) is one of the most important tasks in a warehouse. The units in the interest of their easier handling can be sorted into groups. To classifying SKUs the most frequently used method is the ABC analysis. The traditional approach implements a simplified scheme, which is mostly based on annual dollar usage. Classifying units according to one characteristic can lead to rushed result, because the assignment of stock-keeping units might be influenced by other factors, like number of orders, weight or lead time. In recent years a number of decision models have been developed in order to make it possible to consider more than one criterion in the same time. In general, in the case of representing a model the introduction of the algorithm is more preferable than the visualization of data. However the visualization of results can relieve the comprehension of the given problem and the way, how the model works, even in relation to sorting problems. The aim of this research is to introduce a plotting method which visualizes the results coming from the Case-based distance model developed by Chen, Kilgour and Hipel. This method stregthens the classification by providing separating nonlinear surfaces. Keywords: Case-based Distance Model, Inventory Management, Visualization

Prediction of emerging technologies based on analysis of the US patent citation network

Abstract The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (1) identifies actual clusters of patents: i.e., technological branches, an

Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action

Optimizing the stable behavior of parameter-dependent dynamical systems --- maximal domains of attraction, minimal absorption times

We propose a method for approximating solutions to optimization problems involving the global stability properties of parameter-dependent continuous-time autonomous dynamical systems. The method relies on an approximation of the infinite-state deterministic system by a finite-state non-deterministic one --- a Markov jump process. The key properties of the method are that it does not use any trajectory simulation, and that the parameters and objective function are in a simple (and except for a system of linear equations) explicit relationship