Optimal Control of Multiclass Fluid Queueing Networks: A Machine Learning Approach

We propose a machine learning approach to the optimal control of multiclass fluid queueing networks (MFQNETs) that provides explicit and insightful control policies. We prove that a threshold type optimal policy exists for MFQNET control problems, where the threshold curves are hyperplanes passing through the origin. We use Optimal Classification Trees with hyperplane splits (OCT-H) to learn an optimal control policy for MFQNETs. We use numerical solutions of MFQNET control problems as a training set and apply OCT-H to learn explicit control policies. We report experimental results with up to 33 servers and 99 classes that demonstrate that the learned policies achieve 100\% accuracy on the test set. While the offline training of OCT-H can take days in large networks, the online application takes milliseconds

A Machine Learning Approach to Two-Stage Adaptive Robust Optimization

We propose an approach based on machine learning to solve two-stage linear adaptive robust optimization (ARO) problems with binary here-and-now variables and polyhedral uncertainty sets. We encode the optimal here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now decisions, and the optimal wait-and-see decisions into what we denote as the strategy. We solve multiple similar ARO instances in advance using the column and constraint generation algorithm and extract the optimal strategies to generate a training set. We train a machine learning model that predicts high-quality strategies for the here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now decisions, and the wait-and-see decisions. We also introduce an algorithm to reduce the number of different target classes the machine learning algorithm needs to be trained on. We apply the proposed approach to the facility location, the multi-item inventory control and the unit commitment problems. Our approach solves ARO problems drastically faster than the state-of-the-art algorithms with high accuracy

The Mx/G/1 queue with queue length dependent service times

We deal with the MX/G/1 queue where service times depend on the queue length at the service initiation. By using Markov renewal theory, we derive the queue length distribution at departure epochs. We also obtain the transient queue length distribution at time t and its limiting distribution and the virtual waiting time distribution. The numerical results for transient mean queue length and queue length distributions are given.Bong Dae Choi, Yeong Cheol Kim, Yang Woo Shin, and Charles E. M. Pearc

Characterizing SWCNT Dispersion in Polymer Composites

The new wave of single wall carbon nanotube (SWCNT) infused composites will yield structurally sound multifunctional nanomaterials. The SWCNT network requires thorough dispersion within the polymer matrix in order to maximize the benefits of the nanomaterial. However, before any nanomaterials can be used in aerospace applications a means of quality assurance and quality control must be certified. Quality control certification requires a means of quantification, however, the measurement protocol mandates a method of seeing the dispersion first. We describe here the new tools that we have developed and implemented to first be able to see carbon nanotubes in polymers and second to measure or quantify the dispersion of the nanotubes

Direct Assembly of Modified Proteins on Carbon Nanotubes in an Aqueous Solution

Carbon nanotubes (CNTs) have superior mechanical and electrical properties that have opened up many potential applications. However, poor dispersibility and solubility, due to the substantial van der Waals attraction between tubes, have prevented the use of CNTs in practical applications, especially biotechnology applications. Effective dispersion of CNTs into small bundles or individual tubes in solvents is crucial to ensure homogeneous properties and enable practical applications. In addition to dispersion of CNTs into a solvent, the selection of appropriate solvent, which is compatible with a desired matrix, is an important factor to improve the mechanical, thermal, optical, and electrical properties of CNT-based fibers and composites. In particular, dispersion of CNTs into an aqueous system has been a challenge due to the hydrophobic nature of CNTs. Here we show an effective method for dispersion of both single wall CNTs (SWCNTs) and few wall CNTs (FWCNTs) in an aqueous buffer solution. We also show an assembly of cationized Pt-cored ferritins on the well dispersed CNTs in an aqueous buffer solution