This paper presents a new method for using texture to visualize multidimensional data elements arranged on an underlying threedimensional height field. We hope to use simple texture patterns in combination with other visual features like hue and intensity to increase the number of attribute values we can display simultaneously. Our technique builds perceptual texture elements (or pexels) to represent each data element. Attribute values encoded in the data element are used to vary the appearance of a corresponding pexel. Texture patterns that form when the pexels are displayed can be used to rapidly and accurately explore the dataset. Our pexels are built by controlling three separate texture dimensions: height, density, and regularity. Results from computer graphics, computer vision, and cognitive psychology have identified these dimensions as important for the formation of perceptual texture patterns. We conducted a set of controlled experiments to measure the effectiveness of these dimensions, and to identify any visual interference that may occur when all three are displayed simultaneously at the same spatial location. Results from our experiments show that these dimensions can be used in specific combinations to form perceptual textures for visualizing multidimensional datasets. We demonstrate the effectiveness of our technique by applying it to two real-world visualization environments: tracking typhoon activity in southeast Asia, and analyzing ocean conditions in the northern Pacific.

This research examines the performance of CONWIP versus "push" workload control in a simple, balanced manufacturing flowline. Analytical models and simulation experiments are used to evaluate the tradeoffs between throughput and inventory performance. Tradeoff curves based on inflating the inventory level for the CONWIP system and the arrival rate for the "push" system are generated. As well, the variability of interarrival and processing times are considered as experimental factors. Results show that, contrary to what some previous studies have indicated, CONWIP efficiency is not inherently superior to "push" system efficiency. Instead, the release mechanism used for the "push" system has a significant impact on which system will perform better. Utilization levels and processing time variability also affect the relative performances.

The problem of optimizing decision variables in a singlestage replenishment loop with capacity-constrained batch processing is examined. Simulation and response surface methods are used to model total inventory and delivery performance for a continuous-review reorder point system and a single-card Kanban system. Performance tradeoff curves based on optimal settings are created using nonlinear optimization. The area under these curves is used as a single response for comparison. If tradeoff curves are experimentally replicated, main and interaction effects can also be statistically analyzed. Results show that under time-varying demand the reorder point system performs slightly better. Improvements in performance with setup time reduction are similar for both systems.

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