Instances for the Exam Location Problem

Published: 10 July 2023| Version 1 | DOI: 10.17632/3n92sptjnv.1
Hatice Çalık,


The instances are designed to solve the Exam Location Problem (ELP) with room selection decisions and were originally generated by Mihaylov et al. (2013). The ELP is formally described in the scientific paper titled "The exam location problem: mathematical formulations and variants". The data set can be utilized for solving several ELP variants and extension with or without capacity restrictions. In addition to room, site, exam and participant information, the data files contain parameters which are designated for ELP variants concerned with exam supervisor allocations. More specifically, each file includes the following information: - The maximum number of sites an exam can be allocated. - The minimum number of participants assigned to an exam. - The number of sites. - For each site: Site ID, coordinates, number of rooms, list of rooms. - For each room: Room ID, capacity, fixed cost, variable cost, equipment availability indicator (Boolean), total units of time the room is available. - For each exam: Exam ID, duration, equipment need indicator (Boolean), list of participants. - For each participant: Participant ID, coordinates. - For each supervisor: Supervisor ID, fixed cost, variable cost, coordinates. The file IDs contain information about the number of exams and sites. For each combination, fives instances are generated randomly. We refer to the original paper "The exam location problem: mathematical formulations and variants" for further details regarding the instances. References Çalık, H., Wauters, T., and Vanden Berge, G.. The exam location problem: mathematical formulations and variants, Technical Report, KU Leuven, 2023. Mihaylov, M., Wauters, T., and Vanden Berghe, G. (2013). Geographically distributed exam timetabling. In Proceedings of the Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA).



Operations Research, Mathematical Programming, Automated Planning in Scheduling, Combinatorial Optimization, Discrete Optimization, Integer Programming, Multi-Objective Optimization, Location Analysis, Facility Location, Discrete Location Theory


KU Leuven