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In the Swirltubs case, students apply expected value decision making to a knapsack problem for appliance repairmen. The case is based on a published research paper on work that was actually implemented for a major appliance manufacturer. The case features three parts: (1) problem understanding and definition; (2) optimization results for a small, test problem; and (3) creation and testing of a heuristic for a large-scale implementation that exceeds the limits of Microsoft Excel®. Optionally, an instructor can add risk-analysis simulation and reoptimization under uncertainty in subsequent parts of the project, making it a total of five parts. The case is highly interactive, owing to the relatively unstructured nature of the problem. I have implemented the case over a two- and three-week period format, with upper-level master’s in business administration or master’s in analytics students who have been exposed previously to optimization methods. It has been administered to dozens of students with generally positive feedback. History: This paper has been accepted for the INFORMS Transactions on Education Special Section on Cases Based on Real-World Projects from the INFORMS Journal on Applied Analytics. Supplemental Matertial: Teaching Notes and Excel spreadsheet of solutions are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .

The interactive case study requires student teams to engage with the instructor using a structured decision analysis process in deciding whether to develop a new drug to treat blood clots in legs. There is role-playing in the interactive case study where student teams are decision consultants and the instructor serves as the decision maker, subject matter expert (SME), and coach. Student teams are responsible for managing the analytical process, framing the decision, collecting data from the SME (instructor), constructing the Excel model, assessing probabilities for the most-sensitive uncertainties from the SME, evaluating the Excel-based decision-tree model, and presenting evaluation results and recommendations to the decision maker (instructor). The goal of the case is to improve the analytical, modeling, and consulting skills of the students. The interactive case study is the culmination of a semester-long elective MBA course, entitled Decision Making Under Uncertainty. Since 2010, I have taught this course 31 times to 870 graduate students. History: This paper has been accepted for the INFORMS Transactions on Education Special Section on Cases Based on Real-World Projects from the INFORMS Journal on Applied Analytics. Supplemental Material: Supplemental materials are available at https://doi.org/10.1287/ited.2021.0262ca . The Teaching Note and solutions are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .

History: This paper has been accepted for the INFORMS Transactions on Education Special Section on Cases Based on Real-World Projects from the INFORMS Journal on Applied Analytics.

As the COVID-19 pandemic motivated a shift to virtual teaching, exams have increasingly moved online too. Detecting cheating through collusion is not easy when tech-savvy students take online exams at home and on their own devices. Such online at-home exams may tempt students to collude and share materials and answers. However, online exams’ digital output also enables computer-aided detection of collusion patterns. This paper presents two simple data-driven techniques to analyze exam event logs and essay-form answers. Based on examples from exams in social sciences, we show that such analyses can reveal patterns of student collusion. We suggest using these patterns to quantify the degree of collusion. Finally, we summarize a set of lessons learned about designing and analyzing online exams.

In typical operations research courses, optimization problems, such as transportation and assignment problems, are frequently discussed and taught as stand-alone problems. An integrated approach may prove to be necessary in order to enable students to have a holistic understanding of a complex problem (e.g., a project). In this paper, a global supply network design problem is presented where the case company can source from multiple “suppliers” using multiple modes of transport (including the use of containers with different capacities), allowing lateral supply between warehouses, etc. As more factors are considered, the problem becomes much more complex than any isolated problem in a typical course. The case was tested in an undergraduate course in Australia, and students found this case challenging but at the same time rewarding once solved.

Monte Carlo (MC) simulation is widely used in many different disciplines in order to analyze problems that involve uncertainty. Simulation decomposition has recently provided a simple, but powerful, advancement to the standard Monte Carlo approach. Its value for better informing decision making has been previously shown in the investment-analysis field. In this paper, we demonstrate that simulation decomposition can enhance problem analysis in a wide array of domains by applying it to three very different disciplines: geology, business, and environmental science. Further extensions to such disciplines as engineering, natural sciences, and social sciences are discussed. We propose that by incorporating simulation decomposition into pedagogical practices, we expect students to significantly advance their problem-understanding and problem-solving skills.

The simple vehicle routing problem (VRP) is a common topic of discussion in introductory operations research/management science courses. The VRP can be framed in a variety of ways, and it can be difficult to solve to optimality. For solution purposes, introductory textbooks demonstrate how Excel’s Evolutionary Solver (ES) add-in produces a routing. The ES utilizes a genetic algorithm with a heuristic stopping rule to produce a routing that is not guaranteed to be optimal. Beyond pointing out that search controls, such as maximum execution time, may be extended and followed by restart(s) of ES, textbook treatments do not offer alternative ways to continue the search for a possibly better routing. In this paper, a suite of ways is presented in which students may investigate beyond what ES produces or any other optimality-uncertain VRP solution method. The suite includes perturbation methods and other ways that function within an Excel spreadsheet environment that is popular with students and textbook writers. Because there is no demonstrable feature that confirms optimality, the student problem Solver must settle for a ‘best found’ result as unsettling as it may be. The incertitude is addressed.