A Performance Study on a Multiagent E-Scheduling and Coordination Framework for Maintenance Networks

Many maintenance networks as well as supply networks and virtual enterprises consist of multiple organizations. A common problem arising in these different domains is multiorganization scheduling and coordination. The traditional centralized methods are not appropriate because of the existence of private information and decision-making authorities at different organizations. Although many distributed mechanisms have been presented for supply networks and virtual enterprises, they may not be effective for maintenance networks because of the difficulties of scheduling the tightly related maintenance operations and handling the massive uncertainties involved. These difficulties as well as the heterogeneity of the distributed environment make it challenging to develop an efficient framework for maintenance networks that can obtain a high-quality solution under different conditions, schedule in a timely manner, solve large-scale problems, and so on. In this paper, a price-based multiagent scheduling and coordination framework for maintenance networks is explored, and a systematic experimental study is carried out to evaluate the effects of different factors on its performance. The results show that the framework is able to overcome these difficulties and could be a step toward the next generation of e-scheduling for maintenance networks     

Accelerating Hair Rendering by Learning High-Order Scattered Radiance

Efficiently and accurately rendering hair accounting for multiple scattering is a challenging open problem. Path tracing in hair takes long to converge while other techniques are either too approximate while still being computationally expensive or make assumptions about the scene. We present a technique to infer the higher order scattering in hair in constant time within the path tracing framework, while achieving better computational efficiency. Our method makes no assumptions about the scene and provides control over the renderer's bias & speedup. We achieve this by training a small multilayer perceptron (MLP) to learn the higher-order radiance online, while rendering progresses. We describe how to robustly train this network and thoroughly analyze our resulting renderer's characteristics. We evaluate our method on various hairstyles and lighting conditions. We also compare our method against a recent learning based & a traditional real-time hair rendering method and demonstrate better quantitative & qualitative results. Our method achieves a significant improvement in speed with respect to path tracing, achieving a run-time reduction of 40%-70% while only introducing a small amount of bias.