Designing a computational challenge for students to tackle an online scheduling problem in the form of a case study.

Course Description

In this seminar, groups of students work on an online scheduling problem in the form of a case study. The case study considers the production process of T-shirts consisting of four main stages, namely, cutting, printing, sewing, and packaging. Given a set of orders for producing different types of T-shirts, the objective is to minimize the maximum completion time of the jobs. Due to the operational characteristics of the production process, the setup and processing times of the jobs are stochastic and can be estimated using data collected from sensors. Students are presented with raw data that describes the parameters of the problem. Accordingly, they should tackle the problem. A plethora of Operations Research (OR) and Machine Learning (ML) approaches exist that can be alternatively applied to the case study problem. There will be several lectures to provide students with a broad overview of these approaches. Students should delve into the existing literature on the problem to gain more ideas on how to model and solve the problem. At this point, students will have the freedom to choose any OR and ML approach they prefer to tackle the problem. To make the course more interactive for the students and increase their engagement in the content of the course, there will also be a competition among the groups! Each group competes with other groups in terms of the performance of their solution approach. The winning team wins a prize as well as a certificate. There will be additional lectures on how to appropriately write a scientific report.

My Contribution

• Implemented the simulation environment for the case study
• Created content for the introductory lectures of OR and ML approaches
• Supervised the students during the seminar