This case focuses on process mapping and analysis in a primary healthcare setting to decide on staffing levels of nurses and administrative assistants to support the fixed number of doctors operating in the polyclinic while meeting the limit on total system waiting time. The case describes how different patients interface with the service touch points throughout their stay in the facility. This introduces the students to the complexity and variety of patient journeys within a clinic and gives them the opportunity to build a representative process map to form the basis of a queuing or discrete-event simulation model to analyze the current state. The students are tasked with studying the sensitivity of waiting times to important service and demand parameters, and evaluating possible process improvements such as staff pooling and technology adoption to reduce waiting times. This case is recommended in an undergraduate level course on service or healthcare operations. Supplemental Material: The Teaching Note is available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .
This article presents a data-driven educational approach for supply chain network analytics, focusing on imparting students with a deep understanding of the subject and equipping them with the skills needed for applying data analytics in supply chain management. The framework includes defining learning objectives, designing a teaching plan, selecting effective delivery methods, and allocating necessary resources. Findings demonstrate a systematic teaching approach through real-world classroom experiences and student feedback. It highlights the effectiveness of the teaching methods and tools in preparing students for careers in supply chain management and data analytics. The article offers a blueprint for instructors to create engaging and effective educational programs by incorporating real-world data and industry guest speakers and fostering student engagement. Acknowledging context-specific findings, the study recognizes variations in effectiveness across institutions, student demographics, and cultural contexts. To assess the long-term impact, further research with diverse student cohorts and global perspectives is needed. Practical implications focus on preparing students for supply chain network analytics challenges by integrating data-driven techniques, practical application, collaboration, and industry relevance. The study provides educators a blueprint to prepare students for data-driven supply chain management careers, contributing to supply chain education’s evolution.
Teachers face both push and pull to address large language models (LLMs) in the classroom. The pull comes from the increasing market demand for critically assessing or using generative AI (GAI) tools, whereas the pressure arises from students’ independent adoption of GAI, regardless of stated classroom policy. A case study at Carnegie Mellon University during fall 2023 with 66 diverse students demonstrated significant shifts when LLMs were used. Notably, office hours for technical Python support dropped significantly, and the learning environment became more equitable, allowing students of varying technical backgrounds to progress at similar rates. Additionally, students showed slight improvements in problem framing but no increase in the time spent analyzing results. Surveys conducted postcourse revealed uniform feedback. Students effectively used ChatGPT for coding tasks such as debugging and learning Python syntax. ChatGPT, combined with OptiGuide, showed useful but unpredictable results in modifying existing models. The survey also highlighted time savings in assignments and projects, especially where clear instructions mimicked the prompts needed for ChatGPT. Interestingly, two distinct student profiles emerged: learners who used ChatGPT to enhance understanding and those who sought to expedite course completion, utilizing any time saved for other activities. Funding: This project is funded in part by Carnegie Mellon University’s Safety21 National University Transportation Center, which is sponsored by the U.S. Department of Transportation. It is also supported by the CMU Block Center for Technology and Society and the CMU Eberly Center for Teaching Excellence and Educational Innovation.
Funding: This work was supported by the Division of Civil, Mechanical and Manufacturing Innovation [Grant CMMI-0928936].
The integration of generative artificial intelligence (GenAI) tools into higher education presents both opportunities and challenges, particularly in assessment contexts. This study investigates the impact of GenAI usage on student performance in a graduate-level time series analysis course, focusing on multiple choice exams. Through a controlled experiment involving two student cohorts, each having access to GenAI tools during one of two midterms, we analyze performance outcomes and engagement behaviors using multimodal screen recordings. Statistical analyses reveal that GenAI access correlates with improved scores but only when students are adequately prepared to use the tools effectively. Frequent or prolonged GenAI usage alone did not predict better outcomes, highlighting the importance of AI literacy. Additionally, traditional course materials remained strong predictors of performance across both cohorts. These findings suggest that GenAI can enhance learning when integrated thoughtfully and accompanied by instructional support. The study contributes to the evolving discourse on AI in education by offering empirical insights into its role in assessments and proposing implications for instructional design and policy. Funding: This research has been supported by the Master of Science in Analytics degree at Georgia Tech but we don’t have a specific funding other than this internal support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ited.2025.0166 .
This case introduces Calyber, a simulation-based game designed to provide a hands-on and engaging experience in developing real-time pricing and matching decisions for shared ride services, where multiple riders are pooled into a single vehicle. Students design and implement dynamic pricing and matching policies using a rich historical ridesharing data set, competing for top performance on a holdout test set. Through this case, students gain practical insight into stochastic dynamic decision making within a modern, relevant, and data-driven context. Results from previous class implementations provide strong evidence of enhanced learning and engagement. Funding: This work was supported by the National Science Foundation [Grant 2517861] and the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2022-03524]. Supplemental Material: The Calyber Teaching Note and supplemental files are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .
As part of an institutional initiative to differentiate core undergraduate business classes, we have recently launched a new operations management (OM) course focused on modeling. Whereas the Traditional OM (TOM) course reviews classical models in inventory, queueing, and supply chains, the new Operations Modeling with AI (OMAI) course centers on the practice of mathematical modeling, particularly optimization and simulation. A distinctive element of OMAI is the integration of generative AI–based pair-programming assistants through what we call Incremental Prompting, which prioritizes learning essential modeling skills—abstracting real situations and expressing them with mathematical precision—rather than programming syntax. We evaluated the course through an end-of-term self-assessment survey across both offerings (n = 250; TOM = 156, OMAI = 94). Results indicate that OMAI students reported greater comfort and confidence in understanding, valuing, and applying model-based decision making. Interestingly, these findings become more pronounced after controlling for prior coding experience, suggesting that OMAI benefits both technically experienced and less experienced learners. In survey responses, students credited the pair-programming framework with their increased comfort and understanding of modeling. Taken together, our findings demonstrate how generative AI can be leveraged in OM and OR education to reduce technical barriers and sharpen focus on core modeling skills. Funding: A. Daw was supported by the National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation [Grant CMMI-2441387].
In recent years, instructional practices in Operations Research (OR), Management Science (MS), and Analytics have increasingly shifted toward digital environments, where large and diverse groups of learners make it difficult to provide practice that adapts to individual needs. This paper introduces a method that generates personalized sequences of exercises by selecting, at each step, the exercise most likely to advance a learner's understanding of a targeted skill. The method uses information about the learner and their past performance to guide these choices, and learning progress is measured as the change in estimated skill level before and after each exercise. Using data from an online mathematics tutoring platform, we find that the approach recommends exercises associated with greater skill improvement and adapts effectively to differences across learners. From an instructional perspective, the framework enables personalized practice at scale, highlights exercises with consistently strong learning value, and helps instructors identify learners who may benefit from additional support.
This case investigates the drive-through congestion of a fast-food restaurant with a series of learning modules inspired by computational operations research. Motivated by the renovation of a local Chick-fil-A restaurant, this case evaluates the effectiveness of various drive-through designs in reducing congestion-related problems such as long wait times and large car lines. The objective is to introduce students to a typical operational decision-making process that includes (1) modeling the drive-through operations, (2) encoding the service process using discrete event simulation, (3) predicting congestion metrics with simulators, and (4) prescribing optimal design based on simulator-generated predictions. With minimum mathematical analysis and a flexible amount of programming, this case showcases a cohesive decision-making pipeline and has great potential to spur students’ interest in operations management through a well-known congestion problem. Supplemental Material: The Teaching Note and course materials are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .
Many university courses, especially in science, technology, engineering, and mathematics (STEM) fields, include weekly tutorial sessions, offering students a more interactive and hands-on learning experience. They are taught by teaching assistants, with an increasing trend to consider undergraduate teaching assistants (UTAs). However, challenges hinder tutorial effectiveness. First, UTAs, who are also students, can only apply for teaching tutorials that fit their course schedules. The reduced list of courses that they can apply for may leave the best candidates aside, affecting the effectiveness of the tutorials. Second, as tutorials are not led by professors, they are often scheduled at inconvenient times, potentially increasing absenteeism. To enhance the learning experience of students in tutorial sessions, we propose an administrative framework for the course enrollment and UTA selection processes along with a mathematical optimization model for UTA assignment and tutorial scheduling. This framework allows prospective UTAs to apply for all courses that they want, streamlining the selection process, and the optimization model produces an optimal tutorial schedule that promotes student attendance. We tested our proposed approach on a real case study, resulting in significant improvements over the existing methods. The number of UTA applications increased by 35%, better UTAs were selected, and an improved tutorial schedule was obtained.
Prescriptive analytics has emerged as a powerful tool in decision-making processes across various industries. We present a detailed case study that explores a specific decision-making problem encountered by a cell phone manufacturer. The objective of this study is to demonstrate how case-based learning can enhance relatability and effectiveness in problem-solving, particularly in the realm of prescriptive analytics. The case utilizes prescriptive analytics methodologies and involves modeling and solving the problem using Excel, General Algebraic Modeling Language, or Python. The case study has been successfully integrated into graduate-level courses and has received positive student reviews. Feedback indicates that the case enhances students’ understanding of prescriptive analytics and its real-world applications, fostering improved engagement and learning outcomes. Supplemental Material: The Teaching Note and Excel/GAMS/Python files are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .
Chessboard puzzles involving queens are valuable teaching examples for integer programming. We present the queens domination and peaceable queens problems in an educational format and describe our classroom experience with teaching these problems. We discuss techniques for improving efficiency, such as symmetry breaking, valid inequalities, bounds, and parameters. Funding: This work was supported by the Australian Government (RTP Scholarship).
Having observed many students struggling with the topics of theory of constraints (TOC) and modeling optimization problems, we developed the competitive car production game. It is a tangible interactive in-class educational game, in which student teams compete for the highest profit by managing their own production line of (toy) cars. The objective of the game is to teach topics such as TOC, linear programming (LP), production scheduling, and game theory. Our game is flexible in its application: Students can play a basic version (Module 1) and/or an extended version (Module 2). In both modules, students determine the optimal product mix, deciding how many of each type of car to manufacture while considering production and demand constraints. In Module 2, students also have the opportunity to bid in an auction event for extra capacity and must compete for total market demand. Since September 2023, this game has been successfully implemented in various operations management–related courses taught to Bachelor, Master, and MBA students. Our positive experience with teaching the game, combined with students’ evaluations, confirms its value. Supplemental Material: Additional material for Modules 1 and 2 is available at https://doi.org/10.1287/ited.2024.0125 . The Teaching Note is available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .
This paper illustrates how fundamental concepts from optimization—such as greedy algorithms, matroids, maximum weight matching, and NP-completeness—arise in domains where policymakers wish to select a set of applicants while ensuring representation for specific groups. Examples of such settings include visa lotteries in the United States, the election for Chile’s constitutional assembly, affordable housing lotteries in New York City, selection for Indian civil service positions, and admission to Indian and Brazilian universities. By providing these examples alongside sample exercises, I aim to offer educators tools to make optimization theory accessible to students at all levels, while highlighting its policy relevance. Supplemental Material: The online data files are available at https://doi.org/10.1287/ited.2023.0039 .
Sense of belonging to a discipline can be important to achievement and persistence in the discipline. This research examines how selected course components, implemented during an academic term in applied industrial engineering courses, impact students’ sense of belonging to the discipline. The components include (i) assigning values affirmation exercises, (ii) inviting guest speakers with diverse backgrounds, and (iii) giving reading assignments for journal articles with diverse authorship. Students’ sense of belonging to industrial engineering before and after the course components are delivered is measured with a three-item scale. Changes in sense of belonging during a term in which the course components are delivered (intervention groups) are compared with changes in sense of belonging during a term in courses in which they are not (control groups). Analysis is conducted at the combined level (full intervention and full control group) and also across subgroups created from students’ self-reported identities. The results indicate statistically significant gains in sense of belonging for combined intervention groups and for some subgroups. No statistically significant gains in sense of belonging were detected in control groups.
Having conversations in the classroom about privilege, discrimination, prejudice, and how these issues impact decision making can be difficult. This article presents an activity designed to simulate voter and voice suppression in the classroom. The activity, titled “The Energy Table Who Didn’t Get a Seat,” started by asking students to use concepts from class to decide where to invest money to mitigate the effects of a winter storm on vulnerable communities. Then, the instructor simulated voice suppression, discrimination, macroaggressions, and prejudice in class by excluding certain groups from being able to submit their proposals for consideration. Throughout the class, students were given opportunities to advocate for themselves, yet they rarely did. At the beginning of the activity, students voted for which of their classmates’ proposals they believed was the best. However, by the end of the voter and voice suppression activity, 24%–46% of the students abstained from voting, highlighting the frustration they felt during the simulation. The class concluded with a discussion about how biases impact decision making. After the instructor revealed that the discrimination was part of a simulation, many expressed shock and surprise that they had not advocated for themselves during the activity. Funding: This work was supported by the National Science Foundation [Grants 2121730 and 2315029], the Wimmer Faculty Fellowship from Carnegie Mellon University, and the Division of Civil, Mechanical and Manufacturing Innovation [Grant 2053856]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ited.2023.0053 .
The case addresses a real-world challenge encountered by the nonprofit organization Logica&Co. It revolves around optimizing the logistics involved in collecting food donations from local businesses and delivering them to soup kitchens, utilizing a fleet of bike riders. The focus is identifying efficient strategies to minimize transportation and warehouse costs while maximizing the impact of the donations and private sponsor monetary contributions. The study includes tasks such as determining optimal bike and e-bike warehouse locations and managing the allocation of resources among riders and soup kitchen volunteers.
The article presents a case exercise that teaches students how to apply mathematical programming to a real-life context. The case deals with the management of the food donation supply chain. The case, using a project-based approach, proposes a realistic scenario that simulates the consulting interaction with a nonprofit company, Logica&Co, which acts as a two-sided platform connecting supply and demand. The objective is to define an effective strategy to collect and deliver food donations, using either bike or e-bike, from local businesses to soup kitchens, covering a semester-long timeframe. The form of the problem exhibits nonlinear characteristics, but the design allows for adjustable difficulty levels. Students can assess their performance during the class period thanks to an interactive offline tool, the SoS simulator, which is publicly available for download and can be customized by instructors. The case was proposed as a competitive group challenge for students of the bachelor’s or master’s program in management engineering at Sapienza University of Rome. However, given the embedded characteristics of flexibility, it can be easily adjusted for heterogeneous curricula of undergraduate- and graduate-level courses in engineering programs (this opportunity is extensively discussed in the Case Article and the Teaching Note). The students appreciated both the teaching methodology and the teamwork aspects and highlighted the utility of the SoS simulator tool. Supplemental Material: The Teaching Note and its supplemental material are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .
In view of the long wait times for women and the lack of accessibility for LGBTQ+ individuals when they use restrooms, this case provides a set of analytical tools to evaluate wait time disparity among users for different restroom configurations. A stadium manager who faces complaints about excessive restroom wait times aims to retrofit the restroom layout to improve both efficiency, measured in terms of wait time, and fairness, measured in terms of totalitarian and Rawlsian scores. Given that customers have diverse preferences over the use of restroom types, in three modules, students learn to (i) evaluate queuing parameters for a mix of heterogeneous populations, (ii) evaluate queuing metrics for various restroom layouts and discuss their wait time disparities, and (iii) evaluate and discuss the fairness of access to restroom facilities from a diversity, equity, and inclusion (DEI) perspective. By completing this case, students gain an understanding of service systems, learn about process flexibility concepts, and become familiar with DEI concepts and measures. The primary objectives of the case for students are to understand the trade-offs between efficiency and fairness, develop an understanding of multiobjective problems, and improve their skills in employing queuing concepts and tools. Supplemental Material: The Teaching Note and Excel files are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .
