Operations Research & Manufacturing

Transportation science, supply chain and revenue management, network and combinatorial optimization, and network reliability analysis

ISR is a recognized leader in operations research. Our faculty and students established a model-based systems engineering approach forn integrated product process design, including object-oriented models of system behavior and structure, and optimization-based tradeoff analysis. We also developed formal model checking methodology for validation, verification and safety of hybrid biological and automotive systems. Our systematic approach for computer-aided manufacturability analysis of machined parts has brought the problem of existence of alternative interpretations to the attention of the feature recognition community. We developed efficient algorithms and software, including neural network models, for input-output behavior and model predictive control of chemical processes—making semiconductor wafer fabrication more efficient. Perhaps most significantly, for more than 20 years, as part of the NEXTOR consortium, ISR researchers have conducted operations research for the Federal Aviation Administration in air traffic management and control, aviation economics and policy, and performance evaluation and metrics.

Recent ISR operations research and manufacturing publications

2019

Using Semantic Fluency Models Improves Network Reconstruction Engineering Knowledge

Thurston Sexton, Mark Fuge

The paper directly models a cognitive process by which technicians may record work orders, recovering implied engineering knowledge about system structure by processing written records.

ASME 2019 International Design Engineering Technical Conference/Computers and Information in Engineering Conference

Checking the automated construction of finite element simulations from Dirichlet boundary conditions

Keven Chiu, Mark Fuge

From engineering analysis and topology optimization to generative design and machine learning, many modern computational design approaches require either large amounts of data or a method to generate that data. This paper addresses key issues with automatically generating such data through automating the construction of Finite Element Method (FEM) simulations from Dirichlet boundary conditions.

ASME 2019 International Design Engineering Technical Conference/Computers and Information in Engineering Conference

2019

Least-Cost Influence Maximization on Social Networks

Dilek Günneç, S. Raghavan, Rui Zhang

This paper focuses on the NP-hard “least-cost influence problem (LCIP)”: an influence-maximization problem where the goal is to find the least-expensive way of maximizing influence over a social network.

INFORMS Journal on Computing, Nov. 26, 2019

2020

Consistency Analysis of Sequential Learning Under Approximate Bayesian Inference

Ye Chen, Ilya Ryzhov

Introduces a new consistency theory that interprets approximate Bayesian inference as a form of stochastic approximation (SA) with an additional “bias” term. Derives the first consistency proofs for a suite of approximate Bayesian models.

Operations Research