Advanced Seminar Operations & Supply Chain Management: Online Scheduling Case Challenge

Groups of students work on an online scheduling problem in the form of a case study. Students are presented with raw data that describes the parameters of the problem. A plethora of OR and Machine Learning (ML) approaches exist that can be alternatively applied to the case study problem. Accordingly, there will be several lectures to provide students with a broad overview of these approaches. However, students can choose any OR and ML approach they prefer to tackle the problem. Students are encouraged to delve into the existing literature on the problem. This will not only provide them with more ideas on how to model and solve the problem, but also enhance their understanding of the theoretical underpinnings of the OR and ML approaches they will be using. There will be additional lectures on how to appropriately write a scientific report. To increase student engagement, there will also be a competition among the students. The winning team wins a prize as well as a certificate.

Due to the competition embedded in the course, students learn and implement state-of-the-art solution approaches for the course's online scheduling case challenge. This, in turn, gives them a deep understanding of online problems and equips them with a potent skill set that applies to many similar problems with large relevance for the industry. In developing their solution approach, students learn to critically evaluate different modeling approaches for the problem. By the end of the course, they have a strong judgment of the approaches relevant to online problems. As students work with real-world problem-specific data, students will learn the preparation steps required to turn raw data into relevant model parameters. Students will be able to prepare a scientific report accodring to academic standards and learn how to effectively communicate their findings with their peers.

Students' solution approaches for the course's case challenge are evaluated. The quality of their solution approach determines 40% of the student's grade. Students write a report based on their solution approach for the course's case challenge. The report constitutes 40% of their final grade. The quality of the research, as well as the scientific writing of the report, are considered in grading. For the remaining 20% of the grade, students deliver a scientific presentation based on the report at the end of the semester, including a Q&A.

Previous participation in the modules "Scheduling Manufacturing Systems ", "Management Science,” “Production and Logistics,” and "Modelling, Optimization, and Simulation in Operations Management" or similar modules at other universities are reommended. Basic programming skills (e.g., OPL, CPLEX, C++, Python), knowledge and experience with mathematical modeling and simulation are the requirements of the course. Finally, we emphasize that students should be interested in online scheduling problems, as it is the case study of the course.

To boost students' learning and encourage them to use state-of-the-art solution approaches for the course's case challenge, a competition is embedded in the course. The winning team wins a prize as well as a certificate. The course's case challenge is an online scheduling problem with real-world relevance. Raw data will be provided to the students, and they will take steps to extract relevant model parameters. During several lectures, students gain a broad understanding of the various OR and ML approaches that can be applied to online problems in general. Furthermore, in a separate lecture, they learn how to appropriately write a scientific report. At the mid-term and final presentations, students will have the opportunity to present their work. These presentations provide students with comprehensive feedback on the quality of their work, along with guidance on areas for improvement.