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Aircraft deicing/anti-icing fluids (ADFs) are applied to remove and prevent icing on aircraft during taxi and takeoff. The Dallas-Fort Worth (DFW) International Airport uses deicing pads for deicing activities that collect and contain the spent deicing fluids for proper treatment or disposal. Local waterways receive ADF as “drip and shear” during the aircraft taxi on the runway and then takeoff. The glycol-based ADF serves as a nutrient for bacteria that grow exponentially, deplete dissolved oxygen (DO) from receiving waterways, and subsequently kill aquatic life. In this paper, we present a data-driven discrete-event simulation modeling process developed in collaboration with DFW Airport to assess aircraft assignment strategies to deicing pad locations by monitoring impact on DO. Our process consists of the following phases: (1) Data Collection, (2) Probability Distribution Modeling, and (3) State Transition Modeling. Both Phases (2) and (3) utilized data mining approaches, including treed regression and variable selection via false discovery rate. Detailed implementation of these phases is described for the DFW Airport case study, and the DFW Airport deicing activities simulation tool framework is presented. The actual data and simulation results were compared in terms of the DO levels in airport receiving waterways to verify the model validity after implementing the proposed model for DFW. Thus, the proposed model can be implemented by airports to control and minimize the adverse environmental effects resulting from deicing activities by optimizing the aircraft assignment to the pad locations.

As communities reopen following shelter-in-place orders, they are facing two conflicting objectives. The first is to keep the COVID-19 fatality rate down. The second is to revive the U.S. economy and the livelihood of millions of Americans. In this paper, a team of researchers from the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) at the University of Texas at Arlington, in collaboration with researchers from University of Texas Southwestern Medical Center and Harvard Medical School, has formulated a computationally-efficient optimization framework, referred to as COSMOS COVID-19 Linear Programming (CC19LP), to study the delicate balance between the expected fatality rate and the level of normalcy in the community. Given the disproportionate fatality characteristics of COVID-19 among those in different age groups or with an underlying medical condition or those living with crowding, the key to the CC19LP framework is a focus on “key contacts” that separate individuals at higher risk from the rest of the population. The philosophy of CC19LP lies in maximizing protection of key contacts, so as to shield high-risk individuals from infection. Given the lack of pharmaceutical solutions, i.e., a vaccine or cure, the CC19LP framework minimizes expected fatalities by optimizing the use of non-pharmaceutical interventions, namely COVID-19 testing; personal protective equipment; and social precautions, such as distancing, hand-washing, and face coverings. Low-risk individuals that are not key contacts, including most children, are unrestricted and can choose to participate in pre-pandemic normal activities, which eliminates the need for compliance across the entire population. Consequently, the CC19LP framework demonstrates optimal strategies for protecting high-risk individuals while reopening communities.

This study presents an efficient sparse learning-based pattern recognition framework to recognize the discrete states of three emotions-happy, angry, and neutral emotion-using electroencephalogram (EEG) signals. In affective computing with massive spatiotemporal brainwave signals, a large number of features can be extracted to capture various information from multivariate brain data. However, it is often a challenge to model high-dimensional features efficiently in consideration of the intrinsic structure, such as channel location, feature group, time epoch, etc. In this study, features were extensively extracted from EEG signals and were applied on a structured sparse learning model to perform feature selection and classification simultaneously. An efficient ADMM-based algorithm with a closed-form solution was developed to solve the sparse group model. Experimental results show that the proposed method is capable of selecting a small number of important neural features to discriminate the three emotion states with high classification accuracy. With greatly enhanced interpretability and efficiency to learn neural signatures of brain activity from high-dimensional-feature, low-sample-size brain imaging data, the presented computational framework is promising for handling emotion recognition problems with high-dimensional brain imaging data.

High ground-level ozone concentrations constitute a serious air quality problem in many metropolitan regions. In this paper, we study a stochastic dynamic programming (SDP) formulation of the Atlanta metropolitan ozone pollution problem that seeks to reduce ozone via reductions of nitrogen oxides. The initial SDP formulation involves a 524-dimensional continuous state space, including ozone concentrations that are highly correlated. In prior work, a design and analysis of computer experiments (DACE) based approximate dynamic programming (ADP) solution method was able to conduct dimensionality reduction and value function approximation to enable a computationally-tractable numerical solution. However, this prior work did not address state space multicollinearity. In statistical modeling, high multicollinearity is well-known to adversely affect the generalizability of the constructed model. This issue is relevant whenever an empirical model is trained on data, but is largely ignored in the ADP literature. We propose approaches for addressing the multicollinearity in the Atlanta case study and demonstrate that if high multicollinearity is ignored, the resulting empirical models provide misleading information within the ADP algorithm. Because many SDP applications involve multicollinear continuous state spaces, the lessons learned in our research can guide the development of ADP approaches for a wide variety of SDP problems.

The goal of this research is to find an optimal adaptive treatment strategy to assist physicians in prescribing treatments for patients with chronic pain. This research proposes a two-stage stochastic programming (2SP) method to optimize a treatment procedure for interdisciplinary pain management. The 2SP model incorporates non-convex nonlinear mixed integer constraints, which are constructed based on data from a real pain management program. We derive a piecewise linear approximation method to approximate the non-convex nonlinear constraints in the 2SP model. Consequently, we formulate an equivalent mixed integer linear programming (MILP) model and then solve it using a commercial mixed-integer programming solver. A comparison of the policies generated by the MILP model with the policies generated by the original nonlinear 2SP model shows that, given limited CPU time, the policies generated by the MILP model outperform those of the original nonlinear 2SP model.

Optimizing expensive black-box systems with limited data is an extremely challenging problem. As a resolution, we present a new surrogate optimization approach by addressing two gaps in prior research—unimportant input variables and inefficient treatment of uncertainty associated with the black-box output. We first design a new flexible non-interpolating parsimonious surrogate model using a partitioning-based multivariate adaptive regression splines approach, Tree Knot MARS (TK-MARS). The proposed model is specifically designed for optimization by capturing the structure of the function, bending at near-optimal locations, and is capable of screening unimportant input variables. Furthermore, we develop a novel replication approach called Smart-Replication, to overcome the uncertainty associated with the black-box output. The Smart-Replication approach identifies promising input points to replicate and avoids unnecessary evaluations of other data points. Smart-Replication is agnostic to the choice of a surrogate and can adapt itself to an unknown noise level. Finally to demonstrate the effectiveness of our proposed approaches we consider different complex global optimization test functions from the surrogate optimization literature. The results indicate that TK-MARS outperforms original MARS within a surrogate optimization algorithm and successfully detects important variables. The results also show that although non-interpolating surrogates can mitigate uncertainty, replication is still beneficial for optimizing highly complex black-box functions. The robustness and the quality of the final optimum solution found through Smart-Replication are competitive with that using no replications in environments with low levels of noise and using a fixed number of replications in highly noisy environments.

In order to support the increasing penetration of plug-in electric vehicle (PEV) users, a novel regional PEV charging station system with dc level 3 fast charging is proposed in this paper. To promote sustainable development, the proposed system is designed to be equipped with a distributed energy storage system charged by wind generation, solar photovoltaic (PV) generation, and electricity from the power grid, which can simultaneously charge multiple PEVs. The objective of the proposed system is to minimize operational cost. Wind/solar PV generation and electricity market price are input state variables in this problem, and are predicted by support vector regression (SVR). The uncertainties of the SVR models are analyzed using a martingale model forecast evolution. Finally, bounds of the optimal operational cost in this problem are evaluated with two stochastic measures, which can be solved using the expected value problem and the wait-and-see solution. Bounds from experiments simulating models of the Dallas–Fort Worth metroplex show that the largest uncertainty in the system occurs during weekdays in the summer.

Queuing models provide an attractive and highly-efficient alternative to simulation for quantifying traffic flow efficiency. Stationary Markovian queuing models in which both inter-arrival times and service times are exponentially distributed have been studied by the National Airspace System (NAS). However, stationary queues cannot account for peaks and valleys in demand that are commonly observed in the NAS. Thus time-dependent Markovian queuing models, which aim to capture the variation in demand during a day, have been studied. Furthermore, statistical analysis of real traffic data reveals that inter-arrival times and service times do not follow exponential distributions. As a subclass of phase-type distributions, Coxian distributions with the advantage of closely approximating any distribution without violating the Markov property, have gained special importance on research in queuing systems. In this research, time-dependent Coxian queuing models Cm(t)(t)/Ck/s/s for modeling the en route phases of flight are developed as well, which are approximated by a piecewise constant Coxian inter-arrival time distribution and a time-invariant Coxian service time distribution. Both arrival rates and service rates are calibrated from data extracted from high-fidelity simulation runs driven by actual flying data. The number of aircraft in the system is regarded as a measure of the accuracy of queuing performance. Comparison results between time-dependent Markovian and Coxian queuing models are given in this paper. This study shows that time-dependent Markovian queues capture the variation in demand as well as Coxian queues, with the advantage of mathematical and computational tractability.

Interdisciplinary pain management combines multiple disciplines of professionals to understand the biological and psychosocial factors causing a patient's pain and to determine the best tre...

We study the routing of Unmanned Aerial Vehicles (UAVs) in the presence of the risk of enemy threats. The main goal is to find optimal routes that consider targets visited, threat exposure, and travel time. We formulate a mixed integer linear program that maximizes the total number of visited targets for multiple UAVs, while limiting both the route travel time for each UAV and the total threat exposure level for all UAVs to predetermined constant parameters. The formulation considers a set covering vehicle routing problem where the risk of threat exposure and the travel time are modeled for each edge in a vehicle routing network. To reduce threat exposure, waypoints are generated within the network so routes can avoid high-risk edges. We propose several waypoint generation methods. Using the candidate waypoints, the UAV routes are optimized with branch-and-cut-and-price (BCP) methodology. Minimum dependent set constraints and a simple path heuristic are used to improve the computational efficiency of the BCP algorithm. Computational results are presented, which show that the BCP algorithm performs best when the number of waypoints generated a priori is about half the number of targets.

Autonomous vehicles (AVs) utilize new technology that promises to enhance transportation equity, accessibility, and safety; however, their acceptance by consumers, as well as their future performance, is reliant upon the planners understanding the public’s perceptions of the current transportation services. Most of the existing studies that evaluate the public's opinions and perceptions of AV technology were conducted by using stated preference surveys in metropolitan areas with multiple public transit options; little emphasis was given to a qualitative approach that included all of the potential users. This study explores the concerns and preferences of future riders about the integration of a shared self-driving shuttle into an existing ridesharing service in a city with no existing public transit system, multiple population segments, and potential users. Utilizing a qualitative approach, we conducted three focus groups that were comprised of the general public; university faculty, staff, and students; and people with disabilities in Arlington, Texas (n = 24). A content analysis method was implemented to analyze the data and identify the main themes and subthemes related to each discussion. The results revealed that all three groups identified service accessibility, flexibility, and reliability as the factors that most strongly shape the demand for existing transportation services. Additionally, people with disabilities cited the capacity of the existing paratransit service and the lack of access to distant health care facilities as barriers that frequently prevent them from using the service. Participants also expressed their concerns about the proposed shared autonomous vehicle (SAV) service, and while accessibility and safety were the primary concerns, they also worried about the capacity of the service, the trip cost, and provisions for people with disabilities. Participants with disabilities indicated that they would adopt the SAV as long as service planners provide a supportive environment such as access to sidewalks, ramps, and curb cuts in the pick-up and drop-off locations. This study provides insights into transportation strategies that can integrate SAVs into existing on-demand ridesharing services to improve people's mobility needs. To predict the short-term and long-term adoption of an SAV deployment, it is essential to identify the potential users' concerns, preferences, and expectations of self-driving technology. The research findings are expected to support transportation planners and policymakers in their quest to recognize and utilize the most effective ways to promote the efficiency of SAV services.

Airplane deicing is a safety measure to eliminate/prevent icing on airplanes that can lead to airflow disruption and emergency conditions. Aircraft deicing/anti-icing fluids (ADF) are high in glycol content. At Dallas Fort Worth International Airport (DFW), the major aircraft deicing activities are conducted at designated deicing pads called Source Isolation Deicing Sites, where ADF run-off can be captured and conveyed into the airport’s glycol collection system. A portion of ADF drips from the aircraft during taxiing and shears off the aircraft during take-off, entering nearby waterways without treatment. Glycol acts as a nutrient for bacteria in the airport’s receiving waterways, resulting in an increase in bacterial growth and a subsequent reduction in dissolved oxygen (DO), potentially endangering aquatic life. This paper proposes a prototype data-driven deicing activities management framework for DFW to address the complexity of airport deicing operations and its impacts. The proposed framework uses stochastic dynamic programming (SDP) to assign airplanes in each hour to deicing pad locations, so as to maximize DO in the receiving waters, subject to airport constraints. Some data were artificially generated using the available knowledge of airport operations. The state transition equations in SDP were estimated. The proposed framework was demonstrated using three cases during major deicing events. Improvements in DO compared with actual DO recorded in the data were mixed; however, the results motivated DFW to implement a new data collection process to replace the artificially-generated data, so that a more accurate optimization could be conducted in the future.

EEG source imaging integrates temporal and spatial components of EEG to localize the generating source of electrical potentials based on recorded EEG data on the scalp. As EEG sensors can't directly measure activated brain sources, many approaches were proposed to estimate brain source activation pattern given EEG data. However, since most part of the brain activity is composed of the spontaneous non-task related activations, true task caused activation sources will be corrupted in strong background signal. For decades, the EEG inverse problem was solved in an unsupervised way without any utilization of the label information that represents different brain states. We propose that by leveraging label information, the task related discriminative sources can be much better retrieved among strong spontaneous background signals. A novel model for solving EEG inverse problem called Laplacian Graph Regularized Discriminative Source Reconstruction which aims to explicitly extract the discriminative sources by implicitly coding the label information into the graph regularization term. The proposed model can be generally extended with different assumptions. The extension of our framework is applied to VB-SCCD model which aim to estimate extended brain sources by including a spatial total variation regularization term. Simulated results show the effectiveness of the proposed framework.

Multivariate adaptive regression splines (MARS) is a statistical modeling approach with wide-ranging real-world applications. In the MARS model building process, knot positioning is a critical step that potentially affects the accuracy of the final MARS model. Identifying well-positioned knots entails assessing the quality of many knots in each model building iteration, which requires intensive computational effort. By exploring the change in the residual sum of squares (RSS) within MARS, we find that local optima from previous iterations can be very close to those of the current iteration. In our approach, the prior change in RSS information is used to “warm start” an optimal knot positioning. We propose two methods for MARS knot positioning. The first method is a hill climbing method (HCM), which ignores prior change in RSS information. The second method is a hill climbing method using prior change in RSS information (PHCM). Numerical experiments are conducted on data with up to 30 dimensions. Our results show that both versions of hill climbing methods outperform a standard MARS knot selection method on datasets with different noise levels. Further, PHCM using prior change in RSS information performs best in both accuracy and computational speed. In addition, an open source Python code will be available upon acceptance of the paper on GitHub ( https://github.com/JuXinglong/MARSHC ).

As agencies consider strategies for meeting the livability goals (e.g., public health, mobility, and access) of communities, they often repurpose the existing right-of-way of arterials to meet traf...

Multivariate adaptive regression spline (MARS) is a statistical modeling method used to represent a complex system. More recently, a version of MARS was modified to be piecewise linear. This paper presents a mixed integer linear program, called MARSOPT, that optimizes a non-convex piecewise linear MARS model subject to constraints that include both linear regression models and piecewise linear MARS models. MARSOPT is customized for an automotive crash safety system design problem for a major US automaker and solved using branch and bound. The solutions from MARSOPT are compared with those from customized genetic algorithms.

Posterior constraint optimal selection techniques (COSTs) are developed for nonnegative linear programming problems (NNLPs), and a geometric interpretation is provided. The posterior approach is used in both a dynamic and non-dynamic active-set framework. The computational performance of these methods is compared with the CPLEX standard linear programming algorithms, with two most-violated constraint approaches, and with previously developed COST algorithms for large-scale problems.

A mixed integer programming model is proposed for land use optimization.The mixed integer programming model maximizes suitability as well as manage sprawl.Benders' decomposition is employed to solve large-scale quadratic MIP.Results are presented for a case study for Leander, Texas.Relationships between factors that affect sprawl and land suitability are analyzed. Sprawl has a detrimental effect on quality of life and the environment. With dwindling resources and increasing populations, we must manage sprawl. Ewing et al. (2000) defined factors to measure sprawl in the present urban structure. The measures are divided into four broad categories, which are density factors, mixed use factors, street factors, and center factors, and can be used in future planning of metro areas. In this research, we develop a mixed integer programming model to optimize land usage subject to sprawl constraints, which are based upon the aforementioned sprawl measures. Due to the large size of the problem, we employ a combination of heuristics and Benders' decomposition similar to one described by Bazaraa and Sherali (1982) to provide an urban planner with suitable land use assignments. We show examples demonstrating how the planner can use this approach to analyze how various factors that affect land use and sprawl measures. Finally, we discuss topics of future research.

The approximate dynamic programming (ADP) method based on the design and analysis of computer experiments (DACE) approach has been demonstrated as an effective method to solve multistage decision-making problems in the literature. However, this method is still not efficient for infinite-horizon optimization considering the required large volume of sampling in the state space and high-quality value function identification. Therefore, we propose a sequential sampling algorithm and embed it into a DACE-based ADP method to obtain a high-quality value function approximation. Considering the limitations of the traditional stopping criterion (Bellman error bound), we further propose a 45-degree line stopping criterion to terminate value iteration early by identifying an optimally equivalent value function. A comparison of the computational results with those of other three existing policies indicates that the proposed sampling algorithm and stopping criterion can determine a high-quality ADP policy. Finally, we discuss the extrapolation issue of the value function approximated by multivariate adaptive regression splines, the results of which further demonstrate the quality of the ADP policy generated in this study.

When a traffic incident occurs, congestion starts to disseminate around the incident location. Considering a suitable area to assess the impact of incidents and develop traffic network pred...

To infer latent brain source activation patterns under different cognitive tasks is an integral step to understand how our brain works. Traditional electroencephalogram (EEG) Source Imaging (ESI) methods usually do not distinguish task-related and spurious non-task-related sources that jointly generate EEG signals, which inevitably yield misleading reconstructed activation patterns. In this research, we assume that the task-related source signal intrinsically has a low-rank property, which is exploited to infer the true task-related EEG sources location. Although the true task-related source signal is sparse and low-rank, the contribution of spurious sources scattering over the source space with intermittent activation patterns makes the actual source space lose the low-rank property. To reconstruct a low-rank true source, we propose a novel ESI model that involves a spatial low-rank representation and a temporal Laplacian graph regularization, the latter of which guarantees the temporal smoothness of the source signal and eliminate the spurious ones. To solve the proposed model, an augmented Lagrangian objective function is formulated and an algorithm in the framework of alternating direction method of multipliers (ADMM) is proposed. Numerical results illustrate the effectivenesks of the proposed method in terms of reconstruction accuracy with high efficiency.

A discrete event simulation model and a decision optimizer that were developed for a General Motors paint shop conveyor system are presented. The simulation model interacts with the decision optimizer at four critical points in the system, trying to regroup batches of different colored vehicles. The decision optimizer employs dynamic programming and integer programming to optimize vehicle routing policies. Simulation results of the current decision making policies are compared with those of the proposed optimized policies showing that the number of paint head changes can be significantly reduced resulting in substantial savings on paint head cleaners and paint.

Visceral leishmaniasis (VL) is the most deadly form of the leishmaniasis family of diseases, which affects numerous developing countries. The Indian state of Bihar has the highest prevalence and mortality rate of VL in the world. Insecticide spraying is believed to be an effective vector control program for controlling the spread of VL in Bihar; however, it is expensive and less effective if not implemented systematically. This study develops and analyzes a novel optimization model for VL control in Bihar that identifies an optimal (best possible) allocation of chosen insecticide (dichlorodiphenyltrichloroethane [DDT] or deltamethrin) based on the sizes of human and cattle populations in the region. The model maximizes the insecticide-induced sandfly death rate in human and cattle dwellings while staying within the current state budget for VL vector control efforts. The model results suggest that deltamethrin might not be a good replacement for DDT because the insecticide-induced sandfly deaths are 3.72 times more in case of DDT even after 90 days post spray. Different insecticide allocation strategies between the two types of sites (houses and cattle sheds) are suggested based on the state VL-control budget and have a direct implication on VL elimination efforts in a resource-limited region.

This paper develops an analytical model for a residential microgrid (RMG) under a collaborative environment. The model assumes that the RMG community is under a social agreement referred to as Collaborative Consumption. The model includes a framework for RMGs using a unique method of demand response based on the particular characteristics of residential loads. Residential loads are categorized into three types based on their necessity and reschedulable ability. Consumers assign priority to their appliances. Then, the microgrid informs consumers about their real-time consumption and economic benefits associated with their participation in collaborative consumption. Accordingly, consumers can evaluate suggested alternatives prior to using appliances and consequentially make better decisions. Finally, the effects of model parameters on profit maximization are studied. The results of this paper estimate the economic benefits within a collaborative environment.

We describe an active-set, cutting-plane approach called Constraint Optimal Selection Techniques (COSTs) and develop an efficient new COST for solving nonnegative linear programming problems. We give a geometric interpretation of the new selection rule and provide computational comparisons of the new COST with existing linear programming algorithms for some large-scale sample problems.

An efficient active-set approach is presented for both nonnegative and general linear programming by adding varying numbers of constraints at each iteration. Computational experiments demonstrate that the proposed approach is significantly faster than previous active-set and standard linear programming algorithms.

EEG serves as an essential tool for brain source localization due to its high temporal resolution. However, the inference of brain activities from the EEG data is, in general, a challenging ill-posed inverse problem. To better retrieve task related discriminative source patches from strong spontaneous background signals, we propose a novel EEG source imaging model based on spatial and temporal graph structures. In particular, graph fractional-order total variation (gFOTV) is used to enhance spatial smoothness, and the label information of brain state is enclosed in a temporal graph regularization term to guarantee intra-class consistency of estimated sources. The proposed model is efficiently solved by the alternating direction method of multipliers (ADMM). A two-stage algorithm is proposed as well to further improve the result. Numerical experiments have shown that our method localizes source extents more effectively than the benchmark methods.

It is of great significance to infer activation extents under different cognitive tasks in neuroscience research as well as clinical applications. However, the EEG electrodes measure electrical potentials on the scalp instead of directly measuring activities of brain sources. To infer the activated cortex sources given the EEG data, many approaches were proposed with different neurophysiological assumptions. Traditionally, the EEG inverse problem was solved in an unsupervised way without any utilization of the brain status label information. We propose that by leveraging label information, the task related discriminative extended source patches can be much better retrieved from strong spontaneous background signals. In particular, to find task related source extents, a novel supervised EEG source imaging model called Graph regularized Variation-Based Sparse Cortical Current Density (GVB-SCCD) was proposed to explicitly extract the discriminative source extents by embedding the label information into the graph regularization term. The graph regularization was derived from the constraint that requires consistency for all the solutions on different time points within the same class. An optimization algorithm based on the alternating direction method of multipliers (ADMM) is derived to solve the GVB-SCCD model. Numerical results show the effectiveness of our proposed framework.

As Electroencephalography (EEG) is a non-invasive brain imaging technique that records the electric field on the scalp instead of direct measuring activities of brain voxels on the cortex, many approaches were proposed to estimate the activated sources due to its significance in neuroscience research and clinical applications. However, since most part of the brain activity is composed of the spontaneous neural activities or non-task related activations, true task relevant activation sources can be very challenging to be discovered given strong background signals. For decades, the EEG source imaging problem was solved in an unsupervised way without taking into consideration the label information that representing different brain states (e.g. happiness, sadness, and surprise). A novel model for solving EEG inverse problem called Graph Regularized Discriminative Source Imaging (GRDSI) was proposed, which aims to explicitly extract the discriminative sources by implicitly coding the label information into the graph regularization term. The proposed model is capable of estimating the discriminative brain sources under different brain states and encouraging intra-class consistency. Simulation results show the effectiveness of our proposed framework in retrieving the discriminative sources.