2021
Lu Dai, Mark Hansen, Michael Ball, David Lovell
The authors apply machine learning algorithms to model the system delay and predict high delay days in the U.S. National Airspace System for the 2010s. A broader scope of factors that may affect the system delay is examined, including queueing delays, terminal conditions, convective weather, wind, traffic volume, and special events. They train models to relate the system delay to these features spatially and temporally, and compare the performance of penalized regressions, kernelized support vector regressions, and ensemble regressions.
14th USA/Europe Air Traffic Management Research and Development Seminar (ATM2021)
Yulin Liu, Mark Hansen, Michael Ball, Thomas Vossen
En route inefficiency is measured in terms of extra distance flown by an aircraft, above a benchmark distance that relates to the theoretical shortest distance route (great circle route). In this paper, the authors explore causal relations among en route inefficiency with multiple identified sources: convective weather, wind, miles-in-trail (MIT) restrictions, airspace flow programs (AFPs) and special activity airspace (SAA). They propose two mechanisms – strategic route choice and tactical reroute – to ascribe flight en route inefficiency to these factors.
Transportation Research Part B: Methodological
Michael Ball, Thomas Vossen
The researchers use the slot exchange concept as a starting point to present and analyze two advanced exchange mechanisms. They replace the current one-for-one exchange mechanism, i.e. compression, with a two-for-two exchange mechanism. Subsequently, they add the possibility of monetary side-payments to the exchange. Implementation of the the two-for-two exchange mechanism requires the solution of a mediator problem, which must determine the set of two-for-two offers to accept. The incorporation of monetary side payments represents a fundamental shift from barter to a true marketplace. Supporting such a marketplace requires the development of an appropriate auction mechanism. In both cases, the effectiveness of the exchange mechanisms should be measured by comparing the efficiency of the allocations the mechanisms can achieve with a globally optimal allocation. Each of these topics represents an interesting research challenge, which the authors address.
TRISTAN V: The Fifth Triennial Symposium on Transportation Analysis
2020
Yeming Hao, Sergio Torres, David Lovell, Michael Ball
Traffic flow systems that balance demand versus capacity at busy airports assign Controlled Times of Arrival (CTAs) to incoming flights. This paper evaluates a strategy to assign these CTAs based on user-provided priority lists. The user-provided priority is used to drive a slot swapping algorithm that looks for opportunities to rearrange the order of flights in the CTA queue in a way that decreases delay cost. The authors quantify potential savings by comparing the queue after swapping with the default first-come-first-served rule.
2020 AIAA/IEEE 39th Digital Avionics Systems Conference
Yeming Hao, David Lovell, Michael Ball, Sergio Torres, Gaurav Nagle
This paper proposes a flight scheduling scheme–2-opt-swap, which assigns controlled times of arrival (CTAs) for flights reaching the Freeze Horizon and allows certain slot swapping between different flights with the goal of reducing total controlled arrival delay cost over all carriers. The allowable swaps are predicated on models of carrier preferences following a Collaborative Decision-Making paradigm. Monte Carlo simulations were designed to prove the benefits of this new CTA scheduling scheme, compared to a baseline model of first-come-first-served discipline, which is currently used in Time-Based Flow Management.
2020 International Conference on Research in Air Transportation (ICRAT)
2021
Lida Anna Apergi, Margret Bjarnadottir, John Baras, Bruce Golden, Kelley M Anderson, Jiling Chou, Nawar Shara
This is a study of the engagement of patients with heart failure with voice interface technology. In particular, the authors investigate which patient characteristics are linked to increased technology use.
JMIR mHealth and uHealth
2020
Lida Apergi, Bruce Golden, John Baras, Kenneth Wood
This research tackles the problem of outpatient scheduling in the cardiology department of a large medical center, where patients have to go through a number of diagnostic tests and treatments before they are able to complete a final interventional procedure or surgery. The authors develop an integer programming (IP) formulation to ensure that the outpatients will go through the necessary procedures on time, that they will have enough time to recover after each step, and that their availability will be taken into account.
Operations Research for Health Care
2020
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
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
2021
Yijie Peng, Michael Fu, Jiaqiao Hu, Pierre l’Ecuyer, Bruno Tuffin
The authors apply the generalized likelihood ratio (GLR) methods in Peng et al. (2018) and Peng et al. (2021) to estimate quantile sensitivities. Conditional Monte Carlo and randomized quasi-Monte Carlo methods are used to reduce the variance of the GLR estimators. The proposed methods are applied to a toy example and a stochastic activity network example. Numerical results show that the variance reduction is significant.
INRIA's HAL multi-disciplinary open access archive
Shengyi He, Guangxin Jiang, Henry Lam, Michael Fu
In solving simulation-based stochastic root-finding or optimization problems that involve rare events, such as in extreme quantile estimation, running crude Monte Carlo can be prohibitively inefficient. To address this issue, importance sampling can be employed to drive down the sampling error to a desirable level. However, selecting a good importance sampler requires knowledge of the solution to the problem at hand, which is the goal to begin with and thus forms a circular challenge. We investigate the use of adaptive importance sampling to untie this circularity.
arXiv.org
2020
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
2021
Kevin Chiu, David Anderson, Mark Fuge
The paper unpacks what it means to discover interesting behaviors or functions we do not know about a priori using data from experiments or simulation in a fully unsupervised way. Doing so enables computers to invent or re-invent new or existing mechanical functions given only measurements of physical fields (e.g., pressure or electromagnetic fields) without directly specifying a set of objectives to optimize.
Proceedings of the ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference
Sangeeth Balakrishnan, Francis VanGessel, Zois Boukouvalas, Brian Barnes, Mark Fuge, Peter Chung
Deep learning has shown great potential for generating molecules with desired properties. But the cost and time required to obtain relevant property data have limited study to only a few classes of materials for which extensive data have already been collected. The authors develop a deep learning method that combines a generative model with a property prediction model to fuse small data of one class of molecules with larger data in another class.
Molecular Informatics
Scarlett R. Miller, Samuel T. Hunter, Elizabeth Starkey, Sharath Kumar Ramachandran, Faez Ahmed, Mark Fuge
Design researchers have long sought to understand the mechanisms that support creative idea development. However, one of the key challenges faced by the design community is how to effectively measure the nebulous construct of creativity. The social science and engineering communities have adopted two vastly different approaches to solving this problem, both of which have been deployed throughout engineering design research. This paper compares and contrasts these two approaches using design ratings of nearly 1000 engineering design ideas. While these two methods provide similar ratings of idea quality, there was a statistically significant negative relationship between these methods for ratings of idea novelty. The results also show discrepancies in the reliability and consistency of global ratings of creativity and provide guidance for the deployment of idea ratings in engineering design research and evidence.
ASME Journal of Mechanical Design
2019
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
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
2021
Stephanie Allen, Steven Gabriel, Daria Terekhov
The authors propose a hybrid method involving inverse optimization (IO) as a new approach for a road network’s traffic equilibrium problem and employ a modified two-stage stochastic model to make protection decisions using the IO information. They demonstrate the framework using two types of cost functions for the traffic equilibrium problem and show that accurate parameterizations of cost functions can change spending on protection decisions in most cases, when compared to assuming a uniform cost.
arXiv.org
Saeed Mohammadi, Mohammad Reza Hesamzadeh, Steven Gabriel, Dina Khastieva
Several engineering problems are modeled as linear bilevel programs (linear BLPs) where one problem (upper-level problem) has constrained variables which are optimal solutions of another problem (lower-level problem). The well-known single-level reformulation approach replaces the lower-level linear program with its Karush-Kuhn-Tucker optimality conditions and then linearizes the complementary slackness conditions employing the big-M technique. Although this approach is relatively simple and upstanding, it requires finding the disjunctive parameters (big-M). Regularly heuristic techniques are used to tune the big-M parameters. It is well-known that these techniques could fail, even though they are common. Finding the correct big-M parameters is computational challenging and it is recently shown to be NP-hard in several applications. This research presents a new parameter-free disjunctive decomposition algorithm tailored for the linear BLPs which (1) does not need finding the big-M parameters, (2) guarantees that the obtained solution is optimal to the given linear BLP, and (3) it is computationally advantageous. Our experience shows promising performance of the proposed algorithm in solving several linear BLPs.
31st European Conference on Operational Research (EURO2021)
2020
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
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.
2021
Danae Mitkas, David Lovell, Sandeep Venkatesh, Seth Young
This paper presents details of a novel hardware and software architecture for locally and automatically recording air traffic operations data at general aviation airports, including those that primarily serve smaller aircraft and may not have any other means, such as control towers, to collect such data.
14th USA/Europe Air Traffic Management Research and Development Seminar (ATM2021)
Lu Dai, Mark Hansen, Michael Ball, David Lovell
The authors apply machine learning algorithms to model the system delay and predict high delay days in the U.S. National Airspace System for the 2010s. A broader scope of factors that may affect the system delay is examined, including queueing delays, terminal conditions, convective weather, wind, traffic volume, and special events. They train models to relate the system delay to these features spatially and temporally, and compare the performance of penalized regressions, kernelized support vector regressions, and ensemble regressions.
14th USA/Europe Air Traffic Management Research and Development Seminar (ATM2021)
Danae Mitkas, David Lovell, Sandeep Venkatesh, Seth Young
This paper describes efforts to autonomously classify flight activities at small general aviation airports. The research team has Automatic Dependent Surveillance – Broadcast equipment installed at several small airports in the continental U.S. Data from these devices are consolidated in an Amazon Web Services cloud-based database and computing platform. A number of steps are taken to filter and clean the original data. Individual location records are stitched together to form flights. Supervised learning and clustering techniques are then applied to these flights to classify them according to their operations types. Because small airports are often connected to flight training schools, a particular focus of the project is classifying activities that are more typical of training procedures, and which would be less prevalent at airports where active training activities were not taking place. At the end of the classification process all detected flights receive a label indicating their operation type. This information can later be used to produce counts per operation per airport.
AIAA Aviation 2021 Forum
2020
Yeming Hao, Sergio Torres, David Lovell, Michael Ball
Traffic flow systems that balance demand versus capacity at busy airports assign Controlled Times of Arrival (CTAs) to incoming flights. This paper evaluates a strategy to assign these CTAs based on user-provided priority lists. The user-provided priority is used to drive a slot swapping algorithm that looks for opportunities to rearrange the order of flights in the CTA queue in a way that decreases delay cost. The authors quantify potential savings by comparing the queue after swapping with the default first-come-first-served rule.
2020 AIAA/IEEE 39th Digital Avionics Systems Conference
Yeming Hao, David Lovell, Michael Ball, Sergio Torres, Gaurav Nagle
This paper proposes a flight scheduling scheme–2-opt-swap, which assigns controlled times of arrival (CTAs) for flights reaching the Freeze Horizon and allows certain slot swapping between different flights with the goal of reducing total controlled arrival delay cost over all carriers. The allowable swaps are predicated on models of carrier preferences following a Collaborative Decision-Making paradigm. Monte Carlo simulations were designed to prove the benefits of this new CTA scheduling scheme, compared to a baseline model of first-come-first-served discipline, which is currently used in Time-Based Flow Management.
2020 International Conference on Research in Air Transportation (ICRAT)
2020
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
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
2022
Yimei Fan, Yuan Liao, Ilya Ryzhov, Kunpeng Zhang
In many applications of business and marketing analytics, predictive models are fit using hierarchically structured data: common characteristics of products, customers, or webpages are represented as categorical variables, and each category can be split up into multiple subcategories at a lower level of the hierarchy. The model may thus contain hundreds of thousands of binary variables, necessitating the use of variable selection to screen out large numbers of irrelevant or insignificant features. The authors propose a new dynamic screening method, based on the distance correlation criterion, designed for hierarchical binary data. This method can screen out large parts of the hierarchy at the higher levels, avoiding the need to explore many lower-level features and greatly reducing the computational cost of screening. The practical potential of the method is demonstrated in a case application on user-brand interaction data from Facebook.
Submitted to the Annals of Applied Statistics
2021
Ye Chen, Nikola Markovic, Ilya Ryzhov, Paul Schonfeld
Considers two-stage planning problems in which a resource is first divided among a set of independent regions and then costs are incurred based on the allocation to each region. Costs are assumed to be decreasing in the quantity of the resource, but their precise values are unknown. The researchers develop a new data-driven uncertainty model for monotonic cost functions, which can be used in conjunction with robust optimization to obtain tractable allocation decisions that significantly improve worst-case performance outcomes. The model uses a novel uncertainty set construction that rigorously handles monotonic structure based on a statistical goodness-of-fit test with respect to a given sample of data.
Operations Research
Hua Zheng, Wei Xie, Ilya Ryzhov, Dongming Xie
Biopharmaceutical manufacturing is a rapidly growing industry with impact in virtually all branches of medicine. Biomanufacturing processes require close monitoring and control, in the presence of complex bioprocess dynamics with many interdependent factors, as well as extremely limited data due to the high cost and long duration of experiments. The authors develop a novel model-based reinforcement learning framework that can achieve human-level control in low-data environments.
arXiv.org
2020
Hua Zheng, Ilya Ryzhov, Wei Xie, Judy Zhong
Comorbid chronic conditions are common among people with type 2 diabetes. The authors developed an Artificial Intelligence algorithm, based on Reinforcement Learning, for personalized diabetes and multimorbidity management with strong potential to improve health outcomes relative to current clinical practice.
Operations Research
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
2020
Lida Apergi, Bruce Golden, John Baras, Kenneth Wood
This research tackles the problem of outpatient scheduling in the cardiology department of a large medical center, where patients have to go through a number of diagnostic tests and treatments before they are able to complete a final interventional procedure or surgery. The authors develop an integer programming (IP) formulation to ensure that the outpatients will go through the necessary procedures on time, that they will have enough time to recover after each step, and that their availability will be taken into account.
Operations Research for Health Care
2019
Daniel Lemkin, Benoit Stryckman, Joel Klein, Jason Custer, William Bame, Louis Maranda, Kenneth Wood, Courtney Paulson, Zachary Dezman
Integrating a safety smart list into the electronic health record decreases intensive care unit length of stay and cost.
Journal of Critical Care
