74,460 research outputs found
A General Simulation Framework for Supply Chain Modeling: State of the Art and Case Study
Nowadays there is a large availability of discrete event simulation software
that can be easily used in different domains: from industry to supply chain,
from healthcare to business management, from training to complex systems
design. Simulation engines of commercial discrete event simulation software use
specific rules and logics for simulation time and events management.
Difficulties and limitations come up when commercial discrete event simulation
software are used for modeling complex real world-systems (i.e. supply chains,
industrial plants). The objective of this paper is twofold: first a state of
the art on commercial discrete event simulation software and an overview on
discrete event simulation models development by using general purpose
programming languages are presented; then a Supply Chain Order Performance
Simulator (SCOPS, developed in C++) for investigating the inventory management
problem along the supply chain under different supply chain scenarios is
proposed to readers.Comment: International Journal of Computer Science Issues online at
http://ijcsi.org/articles/A-General-Simulation-Framework-for-Supply-Chain-Modeling-State-of-the-Art-and-Case-Study.ph
Evaluation of two interaction techniques for visualization of dynamic graphs
Several techniques for visualization of dynamic graphs are based on different
spatial arrangements of a temporal sequence of node-link diagrams. Many studies
in the literature have investigated the importance of maintaining the user's
mental map across this temporal sequence, but usually each layout is considered
as a static graph drawing and the effect of user interaction is disregarded. We
conducted a task-based controlled experiment to assess the effectiveness of two
basic interaction techniques: the adjustment of the layout stability and the
highlighting of adjacent nodes and edges. We found that generally both
interaction techniques increase accuracy, sometimes at the cost of longer
completion times, and that the highlighting outclasses the stability adjustment
for many tasks except the most complex ones.Comment: Appears in the Proceedings of the 24th International Symposium on
Graph Drawing and Network Visualization (GD 2016
Investigating the generalizability of EEG-based Cognitive Load Estimation Across Visualizations
We examine if EEG-based cognitive load (CL) estimation is generalizable
across the character, spatial pattern, bar graph and pie chart-based
visualizations for the nback~task. CL is estimated via two recent approaches:
(a) Deep convolutional neural network, and (b) Proximal support vector
machines. Experiments reveal that CL estimation suffers across visualizations
motivating the need for effective machine learning techniques to benchmark
visual interface usability for a given analytic task
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