During the first half of 2020, the lives of people around the world abruptly changed due to COVID-19. Data visualisations and models related to the spread of the disease became ubiquitous. In this paper, we survey 25 different data analytics dashboards, highlight the modelling approach taken by each, and develop a multi-attribute utility theory model to assess their effectiveness in communicating key features that explain the spread of infectious disease. We show that the dashboards that feature dimensions that span the categories associated with compartmental epidemiology models tend to be relatively robust data visualisations, and we highlight that information systems need to be improved to include data on actions to reduce the spread of the disease. We analyse the actions taken by countries around the world and show that when governments employ strict measures early, particularly those that enforce social distancing and include widespread testing and comprehensive contact tracing, they are more likely to experience better outcomes. Recommendations for how countries should respond in future pandemics are detailed.

The Split Delivery Vehicle Routing Problem (SDVRP) allows customers to be assigned to multiple routes. Two hybrid genetic algorithms are developed for the SDVRP and computational results are given for thirty-two data sets from previous literature. With respect to the total travel distance and computer time, the genetic algorithm compares favorably versus a column generation method and a two-phase method.

The Split Delivery Vehicle Routing Problem (SDVRP) is a relaxation of the Capacitated Vehicle Routing Problem (CVRP) where customers may be assigned to multiple routes. A new construction heuristic is developed for the SDVRP and computational results are given for thirty-two data sets from previous literature. With respect to the total travel distance, the construction heuristic compares favorably versus a column generation method and a two-phase method. In addition, the construction heuristic is computationally faster than both previous methods. This construction heuristic could be useful in developing initial solutions, very quickly, for a heuristic, algorithm, or exact procedure

A robust graduate engineering education experience requires students to learn the fundamental subject knowledge, to develop their ability to apply what they know to actual projects, and to contribute to the current body of knowledge by writing theses or dissertations. At the University of Tennessee, Knoxville, Industrial and Systems Engineering students have an opportunity, in the Student Projects with Industry (SPI) program within the Center for Productivity Innovation, to develop their research ideas in conjunction with industry. The success of the SPI program is assessed by evaluating the impact of the SPI program on the preparation of the students, the academic welfare of the department, and the program's economic and social impact on the community. The SPI program provides students with simulated work experiences that enhance their leadership attributes, developing students' critical thinking capabilities, and improving their technical, organizational, and social skills. That these skills enhance students' success both in the university and in their careers is evident in measurable results, including higher student graduation rates, an increased number of publications, and a wider sponsor network for potential funding. The impact can further be measured by the millions of dollars affecting the local economy, in addition to the intangible social benefits gained.