Model introduction | Objective function | Constraints | Group to topic-repetition combination | Defining $z_{ijtr}$ | Number of repetitions per topic | Number of students per group | Per-group skill levels | Binary and non-negativity constraints

Introduction | Diversity-Based Assignment | Dataset 001 (diversity only) | Dataset 001 (skills only) | Dataset 003 | Dataset 004 | Preference-Based Assignment | Dataset 002 | Multi-role Workload Assignment | Full TA and GR workflow | Single-semester

Model introduction | Objective function | Constraints | Group to topic-repetition combination | Number of repetitions per topic | Balanced number of subgroups | Number of students per subgroup | Binary and non-negativity constraints

Introduction | Balancing staff workload and student preferences | OptAssign | Students to elective courses | Assigning students to groups based on traits | Student-to-project supervisor assignment | Automated group assignments in academic setting | Introducing grouper package | Diversity-Based Assignment | Preference-Based Assignment | Multi-role Workload Allocation | Function organisation | References

Model introduction | Model formulation | Objective function | Demand satisfaction | Role-specific workload spread | Annual workload equality | Protected-cohort soft upper bounds | Optional current-semester workload bounds | Package interface | Semester history and capacity | E-allocation scoring | Role-specific terms | Keeping the model small