Electric vehicles have become a trend as a replacement to gasoline-powered vehicles, and been promoted by worldwide policy makers as a solution to combat environmental problems and stimulate economy, whereas the lack of extreme fast charging infrastructure has become one main obstacle to broad adoption of electric vehicles. To promote the commercial success of electric vehicles, effective placement of electric vehicle (EV) charging stations is pivotal. While numerous studies address EV charging station placement, the integration of transportation network traffic, specifically equilibrium traffic assignment, where flows stabilize as drivers seek routes to minimize travel time, has been relatively limited. This research investigates equilibrium traffic assignment with the inclusion of extreme fast charging (XFC) stations and introduces an algorithmic solution. We assess diverse charging station placement strategies, including node-based and network-based approaches, weighing their respective advantages and drawbacks. Extensive experiments on real transportation networks of varying scales validate our algorithm and evaluate different charging station placement strategies. Many interesting findings are drawn from the study. For instance, increasing the number of XFC charging stations may not always result in reduced traffic time; the added value of extra stations beyond a certain threshold can be quite limited. The findings offer valuable insights for strategically deploying EV charging infrastructure, thus promoting electric vehicle adoption.

During the COVID-19 pandemic, Get Us PPE provided a platform aimed at connecting prospective donors of personal protective equipment (PPE) to prospective recipients of PPE. Requests by donors and recipients were collected over time, and periodically, the PPE matching problem was solved in order to instruct each donor to ship a certain quantity of PPE to a given recipient. The objectives of the PPE matching problem include maximizing the recipients’ fill rate, minimizing the total shipping distance, minimizing the holding time of PPE, and minimizing the number of shipments of each donor. This paper presents a software framework to facilitate the development of methodologies to solve the PPE matching problem and their testing on a real-world data set collected by Get Us PPE during the COVID-19 pandemic. Both software and data set are available on GitHub.

Electric vehicles have become a trend as a replacement to gasoline-powered vehicles, and been promoted by worldwide policy makers as a solution to combat environmental problems and stimulate economy, whereas the lack of extreme fast charging infrastructure has become one main obstacle to broad adoption of electric vehicles. To promote the commercial success of electric vehicles, millions of extreme fast charging stations are expected to be established in the next decade in the U.S. However, the widespread of electric vehicles and extreme fast charging infrastructure would impact transportation systems, such as mobility, congestion, equity, toll revenue, and infrastructure maintenance needs. As transportation infrastructure can last for a long time, effective planning must be able to predict the impact of projects and policies decades into the future. Given the nature of transportation systems that is of multiple interacting systems, this study develops mathematical models to quantitatively analyze this complicated and important problem. In particular, we utilize game theory to understand the dependency between the choices made by travelers, and the congestion and delay in the system with respect to traffic assignment. Our research results provide insights and guidance to strategic planning of extreme fast charging infrastructure.

Cloud-based workflow architecture has been widely used in e-science, e-business, smart city, and others, to automate business processes and improve their flexibility and maintainability. Online workflow executes in a collaborative and distributed environment and is prone to fraud and information leakage. Workflow authorization models are implemented to ensure that tasks are performed by authorized subjects with compliance of security/privacy polices. However, existing workflow authorization models have some limitations. First, most of the existing research focuses on static workflows, where an order arriving at a workflow traverses tasks in a fixed sequence. In many real applications, however, task routing is not deterministic, having a probability distribution or pattern that may be estimated from historical data. Second, existing research ignores practical resource constraints, like user utilization, order waiting time, etc. To address the limitations, this article studies the workflow authorization model under the more realistic dynamic settings. We formulate a workflow as a queueing system, so business constraints can be analytically represented, under reasonable assumptions. We model the studied problems as pseudo-Boolean satisfiaiblity problems and investigate their theoretical properties. We also develop algorithms and carry out computational studies. The experimental results show the effectiveness and efficiency of our developed solutions. Our research results are useful for production and process design in many real-life settings such as health care, online banking and electronic payment systems.

Most real world applications comprise databases having multiple tables. It becomes further complicated in the realm of Big Data where related information is spread over different data repositories. However, data mining techniques are usually applied on a single flat table. This work focuses on generating a mining table by aggregating information from multiple local tables and external data sources and automatically generating potentially discriminant features. It extends data aggregation techniques by navigating paths where a single table is traversed multiple times. Such paths are not considered by existing techniques, which results in the loss of several attributes. Our framework also prevents leakage of the class information by avoiding features built after the knowledge of the class label. Experiments are performed on transactional data of a U.S. consumer electronics retailer to predict causes of product returns. In addition, we augmented the dataset with Suppliers information and Reviews to show the value of data integration. The results show that our technique improves classification accuracy and generates discriminant features that mitigate the impact of class imbalance.

Although statistical and machine learning methods require the input data to be in a tabular format, in real-world applications data are often stored across several tables in a relational database. How to build a single mining table from a relational database is a critical pre-processing step of any classification method, because including the right attributes may dramatically boost the accuracy of the classifier. We propose a methodology and implement a software program, Dataconda, to automatically mine a relational database. The user selects a class attribute contained in a table of the database and the procedure builds and selects predictors by exploring the whole database and aggregating information, without any user intervention. For example, our procedure may find that the best predictor for “product return” is the proportion of products returned by the same customer in the past, even if the user has not built any such attribute. Our procedure produces more expressive attributes than existing methods. Our experiments on the ISMS Durable Goods Datasets, a publicly available data set of product returns in retailing, suggest that our method allows new knowledge to emerge.

Metaheuristic approaches based on the neighborhood search escape local optimality by applying predefined rules and constraints, such as tabu restrictions (in tabu search), acceptance criteria (in simulated annealing), and shaking (in variable neighborhood search). We propose a general approach that attempts to learn (off-line) the guiding constraints that, when applied online, will result in effective escape directions from local optima. Given a class of problems, the learning process is performed off-line, and the results are applied to constrained neighborhood searches to guide the solution process out of local optimality. Computational results on the constrained task allocation problem show that adding these guiding constraints to a simple tabu search improves the quality of the solutions found, making the overall method competitive with state-of-the-art methods for this class of problems. We also present a second set of tests on the matrix bandwidth minimization problem.

Drug discovery is the process of designing compounds that have desirable properties, such as activity and nontoxicity. Molecule classification techniques are used along with this process to predict the properties of the compounds to expedite their testing. Ideally, the classification rules found should be accurate and reveal novel chemical properties, but current molecule representation techniques lead to less-than-adequate accuracy and knowledge discovery. This work extends the propositionalization approach recently proposed for multirelational data mining in two ways: it generates expressive attributes exhaustively, and it uses randomization to sample a limited set of complex (“deep”) attributes. Our experimental tests show that the procedure is able to generate meaningful and interpretable attributes from molecular structural data, and that these features are effective for classification purposes.