Exact and metaheuristic methods for a real-world examination timetabling problem

We propose a portfolio of exact and metaheuristic methods for the rich examination timetabling problem introduced by Battistutta et al. (in: Hebrard, Musliu (eds) 17th International conference on the integration of constraint programming, artificial intelligence, and operations research (CPAIOR-2020...

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Bibliographic Details
Published in:Journal of scheduling Vol. 26; no. 4; pp. 353 - 367
Main Authors: Carlsson, Mats, Ceschia, Sara, Di Gaspero, Luca, Mikkelsen, Rasmus Ørnstrup, Schaerf, Andrea, Stidsen, Thomas Jacob Riis
Format: Journal Article
Language:English
Published: New York Springer US 01.08.2023
Springer Nature B.V
Subjects:
Artificial Intelligence
Business and Management
Calculus of Variations and Optimal Control; Optimization
Coding
Core curriculum
Examination timetabling · Constraint programming · Integer programming · Simulated annealing
Heuristic methods
Inspection
Integer programming
Operations research
Operations Research/Decision Theory
Optimization
Programming languages
Scheduling
Simulated annealing
Students
Supply Chain Management
Integer programming
Examination timetabling
Constraint programming
Simulated annealing
ISSN:1094-6136, 1099-1425, 1099-1425
Online Access:Get full text
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Summary:We propose a portfolio of exact and metaheuristic methods for the rich examination timetabling problem introduced by Battistutta et al. (in: Hebrard, Musliu (eds) 17th International conference on the integration of constraint programming, artificial intelligence, and operations research (CPAIOR-2020), LNCS, vol 12296. Springer, Berlin, pp 69–81, 2020). The problem includes several real-world features that arise in Italian universities, such as examinations split into two parts, possible requirements of multiple rooms for a single examination, and unavailabilities and preferences for periods and rooms. We developed a CP model encoded in the MiniZinc modeling language and solved it with Gecode, as well as two MIP models solved with Gurobi. The first MIP model is encoded natively and the second one again in MiniZinc. Finally, we extended the metaheuristic method based on simulated annealing of Battistutta et al. by introducing a new neighborhood relation. We compare the different techniques on the real-world instances provided by Battistutta et al., which have been slightly refined by correcting some semantic issues. Finally, we developed a solution checker that is publicly available, together with all instances and solutions, for inspection and future comparisons.
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ISSN:1094-6136
1099-1425
1099-1425
DOI:10.1007/s10951-023-00778-6