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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Accesso online: | https://arxiv.org/abs/2605.18402 |
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| _version_ | 1866916023018455040 |
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| author | Briand, Cyrille Belaud, Jean-Pierre |
| author_facet | Briand, Cyrille Belaud, Jean-Pierre |
| contents | This paper addresses the Oral Examination Timetabling Problem (OETP) for France's prestigious engineering schools, an organization managed by the Service des Concours Communs Polytechniques (SCCP). The scheduling is highly complex, involving over 7,000 candidates across a four-week period while accounting for constraints such as exam overlaps, geographic origin, and visa requirements. To manage this scale, the paper shows how to model the problem as a Multidimensional Knapsack Problem (MKP) using a Mixed-Integer Linear Programming (MILP) formulation. Their strategy reduces combinatorial complexity by assigning candidates to a predetermined set of pre-validated schedules rather than individual time slots. Experimental results using the SCIP solver on a real 2025 data instance successfully accommodated 7,796 out of 7,804 candidates within a 20-minute time limit. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18402 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Solving a large oral examination timetabling problem using a multidimensional knapsack MILP formulation Briand, Cyrille Belaud, Jean-Pierre Optimization and Control This paper addresses the Oral Examination Timetabling Problem (OETP) for France's prestigious engineering schools, an organization managed by the Service des Concours Communs Polytechniques (SCCP). The scheduling is highly complex, involving over 7,000 candidates across a four-week period while accounting for constraints such as exam overlaps, geographic origin, and visa requirements. To manage this scale, the paper shows how to model the problem as a Multidimensional Knapsack Problem (MKP) using a Mixed-Integer Linear Programming (MILP) formulation. Their strategy reduces combinatorial complexity by assigning candidates to a predetermined set of pre-validated schedules rather than individual time slots. Experimental results using the SCIP solver on a real 2025 data instance successfully accommodated 7,796 out of 7,804 candidates within a 20-minute time limit. |
| title | Solving a large oral examination timetabling problem using a multidimensional knapsack MILP formulation |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2605.18402 |