Operations Research, Decision Making

Decision making, operations research, transportation science, supply chain and revenue management, network reliability and optimization

ISR is a recognized leader in decision making and operations research. Our faculty and students established a model-based systems engineering approach for 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. We have developed models for decision making, including for mass vaccination clinics and other public health needs. Our systematic approach for computer-aided manufacturability analysis of machined parts 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 publications

2020

Iterative Pre-Conditioning to Expedite the Gradient-Descent Method

Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra

The authors propose an iterative pre-conditioning method that significantly reduces the impact of the conditioning of the minimization problem on the convergence rate of the traditional gradient-descent algorithm.

arXiv.org

Decentralized Multi-subsystem Co-design Optimization Using Direct Collocation and Decomposition-based Methods

Tianchen Liu, Shapour Azarm, Nikhil Chopra

Two decentralized (multi-level and bi-level) approaches are formulated to solve multi-subsystem co-design problems, which are based on the direct collocation and decomposition-based optimization methods.

Journal of Mechanical Design

2020

Predictive Modeling for Epidemic Outbreaks

Jian Chen, Michael Fu, Wenhong Zhang, Junhua Zheng

Describes a new discrete-time Markov chain predictive model for the COVID-19 pandemic that provides a way to estimate parameters from available data and is computationally tractable both in terms of parameter estimation and in model output analysis. This model has been adopted by the ﬁrst Shanghai assistance medical team in Wuhan’s Jinyintan Hospital, where the forecasts have been used for preparing and allocating medical staff, ICU beds, ventilators, and other critical care medical resources and for supporting medical management decisions.

Asia-Pacific Journal of Operational Research

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

2020

Lookahead and Hybrid Sample Allocation Procedures for Multiple Attribute Selection Decisions

Jeffrey W. Herrmann, Kunal Mehta

Attributes provide critical information about the alternatives that a decision-maker is considering. When their magnitudes are uncertain, the decision-maker may be unsure about which alternative is truly the best, so measuring the attributes may help the decision-maker make a better decision. This paper considers settings in which each measurement yields one sample of one attribute for one alternative.

arXiv.org

Modeling for emergency public health clinics

Jeffrey W. Herrmann and multiple colleagues

From 2005–2013, Professor Jeffrey W. Herrmann (ME/ISR) worked on public health research with the U.S. Centers for Disease Control and Prevention (CDC); the Montgomery County, Maryland, Advanced Practice Center for Public Health Emergency Preparedness and Response; and the National Association of County and City Health Officials (NACCHO).

Specifically, Herrmann and his colleagues created mathematical and simulation models of mass dispensing and vaccination clinics (also known as points of dispensing or PODs). They also developed decision support tools to help emergency preparedness planners design clinics that have enough capacity to serve residents quickly while avoiding unnecessary congestion.

A poor clinic design has insufficient capacity and long lines of patients waiting for vaccinations or other services such as testing and triage. More patients require more space as they wait to receive treatment. If too many patients are in the clinic, they cause congestion, crowding, confusion, and the potential spread of disease. Herrmann's models and support tools help planners design clinics that have better patient flow and are able to process people more quickly and efficiently.

This research originally was developed with mass dispensing and vaccination clinics in mind. However, the principles behind the models and support tools are useful for those setting up emergency clinics to combat the COVID-19 pandemic. They are offered free of charge.

2020

Fair Liver Transplant Allocation: A Scalable Optimization Model

Shubham Akshat, S. Raghu Raghavan, Sommer Gentry

Presents a new nonlinear integer programming model for U.S. liver transplant allocation a model that allocates donor livers to maximize minimum supply/demand ratios across all transplant centers. In simulations, the model reduces disparities and saves lives.

Raghavan preprint website

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