A General Formulation for the Teaching Assignment Problem: Computational Analysis Over a Real-World Dataset
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910017704165376 |
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| author | Johannesson, Moa Brink, Lina Combrink, Alvin Roselli, Sabino Francesco Fabian, Martin |
| author_facet | Johannesson, Moa Brink, Lina Combrink, Alvin Roselli, Sabino Francesco Fabian, Martin |
| contents | The Teacher Assignment Problem is a combinatorial optimization problem that involves assigning teachers to courses while guaranteeing that all courses are covered, teachers do not teach too few or too many hours, teachers do not switch assigned courses too often and possibly teach the courses they favor. Typically the problem is solved manually, a task that requires several hours every year. In this work we present a mathematical formulation for the problem and an experimental evaluation of the model implemented using state-of-the-art SMT, CP, and MILP solvers. The implementations are tested over a real-world dataset provided by the Division of Systems and Control at Chalmers University of Technology, and produce teacher assignments with smaller workload deviation, a more even workload distribution among the teachers, and a lower number of switched courses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_09605 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A General Formulation for the Teaching Assignment Problem: Computational Analysis Over a Real-World Dataset Johannesson, Moa Brink, Lina Combrink, Alvin Roselli, Sabino Francesco Fabian, Martin Systems and Control The Teacher Assignment Problem is a combinatorial optimization problem that involves assigning teachers to courses while guaranteeing that all courses are covered, teachers do not teach too few or too many hours, teachers do not switch assigned courses too often and possibly teach the courses they favor. Typically the problem is solved manually, a task that requires several hours every year. In this work we present a mathematical formulation for the problem and an experimental evaluation of the model implemented using state-of-the-art SMT, CP, and MILP solvers. The implementations are tested over a real-world dataset provided by the Division of Systems and Control at Chalmers University of Technology, and produce teacher assignments with smaller workload deviation, a more even workload distribution among the teachers, and a lower number of switched courses. |
| title | A General Formulation for the Teaching Assignment Problem: Computational Analysis Over a Real-World Dataset |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2602.09605 |